{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 把脚本和数据放到一个文件夹，可利用相对路径导入数据\n",
    "df_boy = pd.read_excel('体测分数_男生.xls')\n",
    "df_girl = pd.read_excel('体测分数_女生.xls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1、对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.1019960807743185,\n",
       " 1.100793302160504,\n",
       " -1.1013540988127906,\n",
       " 1.101435284122032)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_counts = pd.cut(df_boy['男1000米跑分数'], 3).value_counts()\n",
    "plt.pie(sorted_counts, labels = sorted_counts.index, autopct='%1.1f%%', startangle = 90,\n",
    "        counterclock = False);\n",
    "plt.axis('square')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.1040902900754035,\n",
       " 1.1241672326046372,\n",
       " -1.1108591678396316,\n",
       " 1.1173983548404092)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_counts = pd.cut(df_boy['男引体分数'], 3).value_counts()\n",
    "plt.pie(sorted_counts, labels = sorted_counts.index, autopct='%1.1f%%', startangle = 90,\n",
    "        counterclock = False);\n",
    "plt.axis('square')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 37.,  15., 157., 384.]),\n",
       " array([  0.,  25.,  50.,  75., 100.]),\n",
       " <a list of 4 Patch objects>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_girl['女800米跑分数'], bins=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 19.,  10., 416., 148.]),\n",
       " array([  0.,  25.,  50.,  75., 100.]),\n",
       " <a list of 4 Patch objects>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_girl['女跳远分数'], bins=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>性别</th>\n",
       "      <th>BMI group</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>男</td>\n",
       "      <td>超重</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>男</td>\n",
       "      <td>正常</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>男</td>\n",
       "      <td>低体重</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>男</td>\n",
       "      <td>超重</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>男</td>\n",
       "      <td>正常</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  性别 BMI group  count\n",
       "0  男        超重      1\n",
       "1  男        正常      1\n",
       "2  男       低体重      1\n",
       "3  男        超重      1\n",
       "4  男        正常      1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def BMI_boy(x):\n",
    "    if x <= 16.4:\n",
    "        return '低体重'\n",
    "    elif x < 23.3:\n",
    "        return '正常'\n",
    "    elif x < 26.4:\n",
    "        return '超重'\n",
    "    else:\n",
    "        return '肥胖'\n",
    "\n",
    "def BMI_girl(x):\n",
    "    if x <= 16.4:\n",
    "        return '低体重'\n",
    "    elif x < 22.8:\n",
    "        return '正常'\n",
    "    elif x < 25.3:\n",
    "        return '超重'\n",
    "    else:\n",
    "        return '肥胖'\n",
    "\n",
    "df_boy['BMI group'] = df_boy['BMI'].map(BMI_boy)\n",
    "df_girl['BMI group'] = df_girl['BMI'].map(BMI_girl)\n",
    "\n",
    "# 合并男女数据并做处理\n",
    "data = pd.concat([df_boy[['性别','BMI group']], df_girl[['性别','BMI group']]])\n",
    "data['性别'] = data['性别'].map(lambda x : x.strip())\n",
    "data['count'] = 1\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.103180606926747,\n",
       " 1.1285908755250234,\n",
       " -1.1078191284151728,\n",
       " 1.1239523540365974)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DkgLQdBhEq56mYtv5vHhiGqFiu/MIIVKdMuDkxyL0OW0j3ozhQIHdkUTDkQLQNLiIVv6T4rXjePmUANEKu/MIIRqTEb+PM+SaIjyBo4CNdscRDUMKQOPnJVo5lR2LhvPq+DTiIbvzCCEaoyOuTDLqjiI8gUOBrXbHEfXPsDuAqJU0opWfs+m7EUw+XQ7+QoiD9/3TJjMfbEasahbQ0u44ov7JCEDjFSBaOZM1n/Vm6uV+rKTdeUR9c/kgPa/mrSWkt0Snt4Ss9kmy2mnSWyo8AYVhKpShUEb1HK8yq68X/+PHNW/agmil1pFyTbhUq+AuqCpUVBUYKlwC4RIIl0KotPr98q1gJez+KYj6Nvz/4gy5Zgve9MMBWUzUhEkBaJxcRCs+Y/WnQ5h6uQ/5P2w6XD5o0RNa9kG3HmDR9hBNZhsDf47CdKPjER2PxXRVLElpRKviiKGKQwmKqqIUVkQpDsaoiiYJRhPEkxZJS5NIahKWRmtN3NIkkxrThHSvm+YZHpoHvDQPeMgJuMlKc5Prd9HMj871KSvgVnhcpjJdLgO3D4LFmtKNFoXLUbtWmhSvgcIVULHd7p+cqEvH3xfjkInr8WYMAcrsjiPqhxSAxkcRrfon2+efxuTT0+QVWSOlFGTnQ15vdKt+mraHWbTqYxJoQSIStIpCCb26RJsrd1axprCS1TuDbC2z9+qNAY9Jv7aZ9G+fRfeWGXTNMRN5aSiv12eiFJRssNixUKsdC022zYPtC2XEoDE76eEo/c9ZhTfjaKDK7jii7kkBaGxioQco23gdzx+XRixodxqxv0wPtDsM3WWkRfcxmmZdTW0ldEUwYq0t1+bibUFmrSth0dZyElbj+53skOvnyM65DM7Ppl+eJ5EXMEzD7VPsXGKx+lOlNn2j2DYPErLfTKOhFJz6RIReJy/Gm3ksELE7kqhbUgAak2TsGoJFD/LMsDSCRXanEb9EKWjZF915hKbHSZq2A414NGKtK7X0V+vKzU+X7WRjsb2v6OtbuxwfY/u14uiOmbpbrqm9/oDBrpWaNZ+jNn6l2DIHWbia4pQBZ78SpvOxM/FmjgNksVETIgWg8TiDcNk/efZYv2zvm6Ky86HzsegeY5N0HGYmLUuvK44ybXWFen/RDnZVxexOaKt0r8nYfq04rmeu7tvcVV0IStZZLH8PtXSKQdEauyOKn2N64OL3Q7TsOwVvxkTkAkJNhhSAxmEYseCnvDjWz46FdmcRu8vtjB5wrsXACxRpuaq4Mpz4alPYNWXuNlbulGnTX5Lpc3HG4Dac1Ds32amZz1TBXZqF/9Jq2dtSBlKNJx0umxYkO//veNJuszuOqBtSAFJfH2KhWbx+Xgbrv7Q7iwAItED3PUtz6CWQ1V6tLQonX59XaL6/aCdJ+X06KAoYN6AV5w3Os7rlpRlSBlJQoDlc8VWItOa34fI+ZnccUXtSAFJbe2LB+Xx4QzMWv6HsDuNonnToNQ59yCSLNgON7aXB5JsLi83Xvt9KQn6H6pQCTurfivMOybO65/kNFSyqLgNLpxgUr7U7nrNl58OvZoTxZ09EGW/aHUfUjhSA1JVDtGo+Xz3Yjm//5rI7jCMZLuh6HHrwxUm6jDRLK0PJ91ZUmC98s4lgTNZCNQQFnDKgFRcd3irZMddnsmtVUn3ziMmqfyObX9mkZV+Y9EkYb8ZJgAxLNmJSAFKTn2jl1yz8V18+vtlrdxjH8WaiD5moOfo3JJRLf7khov4+fa3aViansNkp4DG5dmQXTu2dYXlVwuC7x7Wa96IiXGp3NOfpdCxMeL0cT1p/YLPdccTBkQKQekyilR+yfsaxvHmhX3b5a0CZbdFHXW8x+CKjqCqafOrbnea7C3fYnUr8jJE9W3DdsNZWh2YBg+XvW+q7xwwKltody1mO/nWCY363Bm/GYGSPgEZJCkCqiVY9Q+GyC3hpXBpJZ5821mBaD0AP+51Ft9HGkm2V+o+fbVCrC2QFf2PQOtPLLWO66qGdMpQqWmWprx8xZHqgAZ37rzCdjnkHb8YFyOmBjY4UgFSirfOo3PkcTxyRRrTC7jRNX9fj0MNvs3SLXsb0dZX6Tx+vUaWhuN2pxEFwm4rrR3bhrH5ZloeE4uu/oH74h5ISXc88AbjquxCZbW/EdD9jdxxxYKQApI6exELz+MfoNBnKrEfKgP5naz3i9yS92UxZWq7+9vk6oknL7mSijpw+sDW/PqZ1MsMVN/n8Dq0Wva7Q8v9bb5p1gV99FcabPhKYbXccsf+kAKSGNKJVy/j0/zow/2XD7jBNVvcx6BMf1FFPjn7u+13Gi99usjuRqEenDWzNTSPaWP5EuVKf3KpY+W+7IzVdPU6EM/9RjCfQByiwO47YP1IA7KeIVr7Gms9OYcokv91hmqT2h6PHPmQlczqrF38oUk/N2GB3ItGALj6qA1cNydPuyq2oj29WbPza7khN08jb4xzxq0V4M48CZC6tEZACYDcreSnlW/7Gk0MCcmGUOpbbGT32oaRuf6T5wfJy/cePVql4I7zSnqg9BVw/qgsXDG6mjYIlWn1yqyHbatcxZcCF74Zod+iLeALX2h1H7JsUAHv1JxacxXMj0ti1yu4sTYc3Az38NotDJxlzNldZt0xdYVRE5Lr0AtyG4vdju3NynxzYMDOpPrnVlItr1SF/Dlw9K0R6y8tQxmt2xxG/TAqAfTKJVS3nwxvbyDa/dUQZMPB8rU+4X+2oTCavf3u1uX6XjKqIn8oNuHn4zN7J/q39JjMfsNSsxw05dbCOtOwLl34WwhMYAiy2O47YOykA9lBEK95l+fvH8941PrvDNAkteqLHv2TF09vyp2nbjPcWyQY+Yt+O6JTDQ6d2ttIiBUq9falipxyv6kS/szUn/7UAT6A3IFs1pigpAHZIxq6ldNMDPD00QEI20KoVw0Qf9WuLY35nTF9bYd32zgojIfP84gDdckJXzh7YHOb+w1LT7zPk97IOjP1LlAETZuPNGAnIeZgpSApAwzuUWNVXPD3MT8l6u7M0bs27oce/rKv87fS1U9YYS7bJ5kni4LXJ9vHsuT2t1t6ooaZejpwtUEuGCy79LEhenwdx++6xO474KSkADSuHWHAF716dx/J3Zd7/YCkDPeRai+G3Ge8sKeL+j9bKHqSizkw6Op+rh7ZGrfggqT6+2SRSZnekxiuzLVw7J4wn/XBAdjhLMVIAGo4iWvkpi984hn//Vq7wd7CadUGf9ZKuCuTrK99cbazYUWl3ItEENQt4eO783sn8TMNU718HK963O1LjNfhizQn3r8Kb0Q+Q03FSiOw611AS0esp23IUn9wmB/+DoRT6yKstfcU3fFTYjOF/mycHf1FvioMxznh2ofnQzEKs057W+pS/JzE9dsdqnOa/rNi5tD2JyK12RxH/S0YAGkY+8dBynh6aRvE6u7M0Pplt0Oe8akWzu3LtlLXG/M0yJCsaTl66h1cn9rVyEwVKvTpeUSZbSB+wrPZwzfdhPIFDgeV2xxHVZASg/ikiFS/z9SMeOfgfhA5D0FfNYm6kvT7mr3Pl4C8aXGFVjNGPzzemF2air/oWuo+xO1LjU74FPvuDl2jlm4DL7jiimhSA+ncOoaJD+fav8qA/QPrwX2l9wVSemFXMFf9aYibkRCJho99NXaHu/GQL1lkvoo+/z8Iw7Y7UuMx70aBgeUcS0ZvsjiKqyRRA/colFlrPKydnsXWu3VkaD5cXfcrjVqLbWHXlm6vUgs3ldicS4kdts3z88+I+VmbVWqVem6Cokovf7bfsfLh6VhhPYDCw0u44TicjAPUpWvl3Fr3mk4P/Achsg/7VTF3QZrQe/fh8OfiLlLOtPMKov88zZlW1RV/zPXQcanekxqNsE0y700uk4g1AhlBsJgWg/owgET2Nz++QVf/7K/8o9FWz+KIwi7FPLTDlAj4iVVkarn1zuXr0myL0eW+hh94oE1T764fnDYpWdyERvdHuKE4nUwD1w0+sai1vX9aGVR/bnaVR0EdcYTHqLuOhL7fy2pytdscRYr/1a5vJ8xN6ateKdyz1wfUmWrrAPuV0gqu+C+NJGwistjuOU8kIQH2Ih+5mw1fZcvDfD4YLfcbzieSIO9QVr6+Ug79odJZsq2Dc0wtVsNupSk94w8Ilg377VLoBvrhbpgJsJiMAda8/0arZ/H2wXxYH7YPLi57wRjLU8lB15vOLjMLKmN2JhDhoPpfB25cPSLaKrjfU5NMVEVm/8ouUgsumB2nZ93ZcnkftjuNEMgJQt0wiFa/y2e99cvDfB08AffGHVlHOIEY/Pk8O/qLRiyQsxj21wFyaaK/15V9qMlrZHSm1aQ1vXxrAStwHdLE7jhNJAahLyfi1FK/txPyX5UI/v8SXjZ70mV7v6sLYJ+ebkbjMmYqmQQMXv7LU+KrQr/UVX0MzOa79opL18NWDXiIVT9odxYmkANSd9ljxP/LOrwLItMreBVqgL5+ul0Ty9PjnFxlJ+VH9hLYsKtcvQKbnGq8bpqw0pq6Man35l9BmsN1xUtvsJ02S0aGAnE/ZwKQA1A1FpOJFvn3MTdEau7Okrqx26F/N1LNLAnri5KXy2NuLSOEGds6YjFIykNSY3f/xGvXc9yXoiR9Cl1F2x0ldiSh8+vs0IhVPAfKgb0DyJFw3xhAtP5KvH3bbHSRl5XZGXz6D6VsNrnl9uTzufkH5qtk0G3yi3TFEHXj6qw38+Yut6HP+Cb1OliGdvVnyJlTt7AicZXcUJ5GzAGrPIFKxkveu7saKD+zOkpryeqMv+YgPVgb1XR+ulob/M0LbV7Ptk6fBMInsXIe3eXuUywPaql4spRStR15Cen5fu6OKgzCmTx73j+uMeutiWPOZ3XFSU6dj4dx/7cSbng/IquAGIAWg9saza+ULPHFEut1BUlJeb/Skz3h9Uan+y2dr5eC/D7HyQra8/yhdLvwTAAVfv47pC9D8sJNtTiZq69SBrbnjhHzU6xNg/Qy746Smiz8M0uHIP2C6/2p3FCeQodjacRGtfIRPbpOD/8/Jaoee+G/eWlwmB//9VPTDBzQ/4rQfP65YM4esXkfbmEjUlfcW7uChL7eiz/0XdBhid5zU9PHNAaz4PUCW3VGcQApAbWjrInatymbddLuTpB5/DvqST/TMzTHrgU/XyMF/P5m+DAq+fo2Cb96gfOV3eHJa407PtTuWqCOvzdnKE9/sRJ8/BdoMsjtO6ilcDsvfdxEL3W53FCeQKYCD5yUW3Mrk05qzZY7dWVKLy4e+9HO9PN5GX/jyYimZB0gnkxR+9ybbv3iBnL4jaHP85XgyW9gdS9Sh60d10RcPzlbq2eHV58KL/8poDdfND+NJ6wFssTtOUyZPzgcrGb+KLXP8cvDfg2Giz33N2u5qb10kB/8DYiViBDcvY/u056lcv4CeVz1LTv9RbHjtLrZ+/CRWXNZFNRWPfbFOfbCiUutLPoH0lnbHSS2VO+D7p11EKh6yO0pTJyMAByedeGgbz4/OpGCp3VlSij7tSau80zhOeHy+EbfksXUgtn36DGhNVs+jSO/Y/8fbtZWkdOkMcvvLueRNzdMT+liH5VQp9dwIuXbA7rwZcMOyML6sIcAiu+M0VVIADkYiegdrPr2FNy5MsztKKtEjfm9FD71KjXlygaqIJOyOI0Sj8O4Vg5Lt4xtQL5xgkpRRnh8ddrnFqNtn48uSVbD1RIZoD1wu2rqZz++Ug/9u9OCJlnXkNWrCy0vl4C/EARj/3EKzMqMretyjSbuzpJR5LxpEKwcAx9sdpamSAnCgYqH/Y+lUQxbu7Kb7GBjzJ+O6KWvUpuKw3WmEaFTiluacF5eYVq/TDD3oIhmS/Q8rAR/fEiBa8SRg2h2nKZICcGBaobiaL+/32x0kZeR2Rp/5D/4yfSuz15fYnUaIRqmgIsq1U9YoTvyzkosH7Wblh1C2tSVwqt1RmiIpAAciWnUP818xqNhmd5LU4PKhz3tLT1tbab3+w1a70wjRqH2/oZQnvtmBPv8tSGtmd5zUMfOBdMJld9odoymSArD/OqKMC5n5oNfuIKlCj3vE2kkz65apK+RxJEQdeOHbTczbmdR6whsWhox6A9WjAMlYF+AIu6M0NfLEvb+ilfcz+0mTUDIDUR4AACAASURBVLHdSVKC7n+OTvY8VU14cYk8SwlRh658bZmqyOqOHn2vZXeWlGAl4ZtH/UTK/2B3lKZGCsD+aYXhOoNZj8vlfgFa9IBxj6rfTF0rK/6FqGOWhvNfWmpYgycq3fs0WRQIsGCygeE6Duhod5SmRArA/khErmXJFAiX2p3Efu409Hlv6bcWlfDdOln0J0R92F4e5fcfblCc9qSiRU+749gvWgnzXjKIBX9rd5SmRArAvvnQ1nV895jP7iCpQJ/6pLUtnqEf+GSN3VGEaNI+W17Ie0vLtL7wHY1HLjjK7Cc9KGMScqXAOiMFYN8msG2+QdFqu3PYTg+6SCe7jFbnv7RUHjdCNIB7/r1K7Yz5LT3mAdkkqHwrrJ0GyfjldkdpKmQr4F+miFSs5a2JnVn3hd1Z7NWyD/rSaVz71ipmrZepkFST7jVJ97rI8LnI8LlJ97nI8Lqq//S5SPeaiUjccoViSUKxJOFYklAs8d/340mKKmOUheN2fytiD60yvXx41UCM186FDV/ZHcdebQbBxH8X4Qm0BmQBUi257A6Q4oYTKctz/MHfMNHjX7H+vaJczVpfquyO42Rts330aJVBlxYB3a1lerBri0CydbbPbyil4wkrmNC6yrIo1+gyQ6kS01BFfrdZYBqqUmvtjSasnHhSZ1taZ2utM4EMhUo3DAJel9k8nrTU1tJwdHVBlXtNYVXahqIQG4uC7CiPINd2ssfOiihPfbuTq898HvXYIIgF7Y5kn+0LoGi1lzaDzgDetDtOYycjAL8kUjmLL+8/gu+fcvRBTx/9Gys05EZ9zKPzTHm0NBy3qRjUIZuhXZvFD8nPDnVqHvBbWlfFEnppmsf83uMylgGrat7qYlhGAS2BnkCvcCzZP5pIDnKbRlePy8jaUR4JzVpX4puzocQzb1MZcgZIw3r/ioHJtpvfU+rD3zh7Cq7nSXDaU8vxZfUF5CmpFqQA7F2nhJVYl7ASyihYnvR8fofJxq/tztTwcjqir5rFZa+tYMFmuVxpfWud5eOoLrmM6pVXOah9ljeSsNakecwpbtP4FlgI7LIpWgDoZ1l6RGUkcbLfYw7eWR6JfrFyV9pXq4tcS7dVkJTnknrVIt3Dx9cMxnj1LNj0rd1x7KMMuHF5kIzWJwAO/kHUnhSAvYgkIg+9vebt6yYvn+yZ0HOCdVb3swwiFcnAD/8w+fph0M7Yo0NP+sT6KtRJ3fDWckePgtQXBQzOz2Z49+bxET1bRHIDHuJJ6/MMn3sK8BmQqjtPuYEjoonkydG4dZbLNNp8t644+e6C7YHZ60tkuqCeXDSkA78+Ir16KiAesjuOfQ67XDPq9s/wZY2xO0pjJgXg53kjiciu8R+Mz9hYsbH6BtPLiZ1O5LK+l+oW/mb41s3E+ORWRfkWe5PWI93/HB0b8zDDHpmrEvI4qVPZfjenDmxtnXdE+7DPbRb63MY/3abxATAPaIztsq2l9SnBaOLXiaRu/+a8bZ73Fmx37ayI2p2ryXn3VwOs9pvf0erfv3XuLpzuNPjdmgie9L7AOrvjNFZSAH7euYsKFz17wccXZPzcXw5oMYCJfSYmh7UdaurSDUnf9PtNVnzQ0BnrV1ou+vpF3PT+Rr5cVWR3miZjQLsszjuifeiY7s2MeEJ/kO5zPQJ8T9OayxwYjCaucRnqvKXbK6zX5mxN/2p1EQkZFqgTeRlePr56EOqfp8Pm2XbHsc/ou+McetmLeNOvsDtKYyUF4GdURCvm3zXrrkGfb/r8Fz+vma8ZZ/c42zq/1/mGKxGzAoteN5h+HyQiDZS0/uizXrSWpA9VEycvlaH/Wgp4TMb2a6UvOLJDMDfgrvC5zUdMQ70INPWtFP3AWRXh+A1KqZ5T529zvTJrs7s0JKca1tavR3Xhor5urf4+WBEP2x3HHtkd4Jo5Vbj9uYA8qA6CFICf6l0RrZh77BvH+hN6/1Y5u5SLkR1GcmnfS61OWR0N95Y5luuT2wwKl9dz1HrSeQTJc17VI/82T1VGZaX3wfK6DCYc3i552dCOsaTWMzN87oeAL2mcQ/y11SMUS9ykUBf8a84W8+XvNrur5LFVK1/++tBk5sJnUdPvde5UwJXfVNCq3wTgI7ujNEZSAPYQSUQefm3la9c9Mu+Rg7rwT/ec7lzU+6LkCR1PMHXF9qT/60dMFkyu65j1x+1HX79IP/FDlXrh2012p2mUDAXj+rfWvzmuS9g0jK8zfK7fACvtzpUi8quiiQcUnPbCN5vcr83ZYkYSTuxDtXdofjbPnNsT9fdBULnT7jj2qF4M+C6+rDPsjtIYSQH4XyoUDxVe+PGFzVeX1m7r30xPJqd1PU1P7DORNOXSgRXvG3x2B0TK6ihq/dCj7kqW9L2E0Y/Pd+6riloY1q0ZvzuhezDL716T4XNdDcyyO1OK6lkZSTyktR755Iz13qnztxuyRuDA/XNif6tXyTSt3rnCmb+vgeZww7IILl9zwME7JB0cKQD/66jtVds/OeHtE3528d/BUCiGth3KpL6Tkn2b9zHNHUss9+e3Gym5eCc9D339Iia9upxFWyvsTtOo9G2Tyc1juld1ap5WHPC6rgM+pGkt7Ksvgysj8UdjCX3onz5elTZ9pV3bHDROzQMePr32ENRzw2GXQweZLvmokvyjfwW8bneUxsbZO0rtIRQPTZy6ZmpaXd6nRvP1tq+55NNLzDPeP5O3qtbq8IXvELpxWZKjf12XX6rW9LG3JVcVhpJy8N9/6V6Tu0/pFXnmwkGlvdtk/CbgdXUFPkAO/vtrfobPfWyzdM/Ye07pteWh8f1C2f6Dmn1zpKJgjE9XlqLH/sW58yjzX8kgXPoru2M0RjIC8F+uSCJSfMb7Z2Ruqazfc/v9Lj8ndT5JX9p3EjmeLPxrvsD49DZF5Y56/bq/KLsD+po5nPz0AraXNf6zGBrCkM653Hta75DXZbwV8LquB6Q51Y4/HEv+OZ60Lr37gxVpcvrp/vG6FN/ccJg2Xz9XOXK3Um8G/G5tFJevDU3/zJo6JSMA/zVyW9U26vvgDxBOhJmyeoo6cepYdc2X16uvs3Kt6K8XEr3ymyTdjq/3r/9z9Kg7rR82VWg5+O+b321y18m9In8Z329XbsBzasDrmogc/OtC2O8xr8/0u4+/97Te2/98Zt9Qpk+uV7Yv0YRm8rxipU96RKMceNZutBLWTU8AZ9odpbGRAlCjKlY16e01b9fZ3P/+mlcwj2unX2uOfeckXimcrSrHv0Dwd6uTjLoTTE/DhGjeHXqeZPz+/TUOfPY4ML1aZfD2VUeERvVq8U6ax+wKTLM7UxP0bZrH1e3orrmvvHftkNAx3ZrZnSflPTZ9HbG0Vpq+Dj0GLng1QLhUNgQ6QDIFUM0XTUSLT5x6YtqusL2LkNyGm9H5o7m076VW+4y2hmfjd5b5yS0GxfW326U+701rpjVA3Sj7/e+VAi4c0iF5xbGdwl6XcbmhlCw4ahjHhmKJ1z9bVpj9x49W+eRMgb0bf0hbbj0mF/XXPpBw2BbMLi/cvDGKJ60LsM3uOI2FjABUO3F12eqE3Qd/gLgV56MNH3HmB2caEz+ZxCdmTEeu+o7wdfOS9D+37r9g64HojsOMO95fJQf/vfCYBg+N7xe+fFjH5X632U8O/g1qZprH1X1077yZL006JJgbkAWCe/PWvG1UJN1JfcSVzlsQmIjCivctrEQ9PEk2XVIAgIpYxWVTV0/NtDvHnpaXLOfWr281R08ZzTMbP1QlJ/6Jqls2WJz0MHjS6+Rr6BP+ZH28skxXRZN1cn9NTbbfzQuXDA4e3inns4DXdTiw0e5MDlQZ8LrGdm4e+NtbVxwR6tW6wWfqGo27P91sMuwmA5fP7igNb+G//ESrZBrgAMgUAGREk9Fdo94a5S2Ppvb17g1lcEy7Y7i076Rkz9yeprltoeX+9DaD7QsO7g7zj8I6b4o++uEfVDTpvBcN+9I+18+zFw4KZfjcT6V5zJtx5ha+qeaMUCz5yu/fWRaYuVrOEvg5M64flMyYeYeh5r/irFE9ZcDNG8L4swcBq+yO0xjICACcsrBwYSzVD/4AlraYsWUGF358kXn2B+fwTmSLDk/6mNANS5IcedUB358+/v7k+8vL5eD/Mwa0y+LVSw8L5wY8N6Z5zJuQg3+qmJrmMYf/8Yw+Zece1k6GrX7Gk98WmhxzM447I0BbsPhNk0TkQrujNBaOLwDl0fLzPlz/YaMbU9xQsYF7Z99rDn9jOI+umGzsGHq9Dt62WevTn9MEWuz7DtoMQrfoYf7lkzX1H7aRGd07Tz95/sBgus91uts0nrE7j/iJuX63OfjakZ233XR8N4etdtu3N+dtI+bOhC6j7I7S8Ba/5iEZn0T1ul2xD3VSAJRSHff42FBKjVDKvgqqlGqplPLu9nHnn/k0l8/lG/7ttm8bMFndCiVCvL7qdXX82yeo62fcqL5r3saK3rCU2BUzk3Qevtd/p4fekJyzJWSFE/IianfnH9E+cdfJvUr8HvNo4FO784i92pDmcQ06dWDrVbeM6S4lYA9vLC5X+thbnDe/u20+JGNZQDe7ozQGtS4ASqlbgMF73NwXuEPXcoGBUmqhUmqaUmqXUmqoUmrGbm/v7/Z5i2o+b7ZS6gilVA/gPqCbUuoUpVQOcLJS6rw9vsSRO6p2pMTq/7owZ+ccrpx2lTnu3ZN5tXiBCp77KsGbViUZfisYu22oktEKup1g/vHjtY4fAdrdGYPaJK8e3rnE7zEHAYvsziP2qSTgdR07rn+rTdeO6ByzO0wqeWL6OnReH0XLvnZHaXjrpgM4cPjjwNXqAFDzyr+91nrqHn81DnihNvddY6vW+jjge8ADTAG+Aa4Ewrt93paaz9tG9VztpUCU6v3YbwQqtdZ/A05SSv043B9NRk/5fNPndbr3fyrYGdzJI/MeMY5541juX/CYuX7gOVbo1k06OeF1TU5H9OFXWptLQsmtpeF935lDjOrVQv/2+G4VNa/86387SFFXygJe19BzD2+3Y9LR+Qm7w6SKuKX5cl0lethvnbd2Zc3naYRKTrM7RmNQ21eAFwJPACilBte8Mp8G3ABcUfOqfHrN7dOVUkcf4P23rrm/I/jpIqzdRxeaK6VeAppprX8AFv/nduARrfV/nhj+Cfz4wIgmomfN3Dqzye41GrNifLD+A05971Tj0s8vV194TSt6zRwiR1xuPDhtszMvH/ozjuiUwz2n9A76PeYIYK3decQB25XmcR01aWjHXbIw8L/+/Mla6HGiQXpLu6M0rA1fgTvtaGSN2z7V9gfURWu9AkBrPV9rPRy4BPhBaz205lX5J8BrWuuRWusDnWwv3G0E4JcUaa0nAkVKqTuAicCpwJ+A3yil/jMfNBsYVPN+W5fhar2kaMkBRmqclhYt5bczf2s+OPdBqmJJa9Z6uWYGQL+2mTx8dr+Q32OeiAz7N2bb0zzmUdeN7FJ6yoDWznvV+zOKgjHW7wpZ+sirnfXzqNwBoWINDLA7Sqqrj4Z0JfD4bh+PBd49yPvaczP8E4ChNV9j90V92Uqpu4Bs4P6a0vAeMAk4jv++qgsD/pr3R8/ZOSdhaWf9bpzb41zrnfkl0oyBLi0CPHH+wHCaxzWe6qkl0bht9HvMo28Z0738+N55zlsA9zMenr7Z4LDLDNxNbqbzl635zI2VPM7uGKmutgeCsFJqzy3pSoFblVK/U0qdDKzXWhcc5P33VErNAIbUfLwYuBZ4Htj99KwdwANAOfAXpdQnwMnAc8DHwM01n9eJmvndimjFqTO2zKib7fQaiZ65PWmb3tb4x9cb7Y5iu9ZZPp67aHDY7zYvBz6yO4+oM6v9HvPYO0/uFeotOwYye0MpwVgySb+z7I7SsNZN9xIpP9XuGKmutgXgI/a4BKPW+iFgZM19vwN4lVJtD/SOlVIBYFnNtMKsmpt3aK2Xaq2XAu8ppXw1p/pVaK0j1V9e36i1HgN8AFyutT5Ba/3nmn9/NvAhoNyme8TsHbMP+BtuzC7odUFi5fZwMu7wC6q4TcVjE/oHA17zTtNQr9qdR9S5JX6PedHfzu0vlxMG3l1WbupDJjlrbcTGr8ETOJSfjiKL3dS2AHxI9cr6PACllFcpNQT4I9WnYQykeuHdVKXUIzUH7LZKqb/ux32PABYopToBzaheBLh7kbgAuIrqsrFYKdUM2L3y/88eBEqp7kBbrfVioHcwHjS3VTnnolFuw83xHY93Pf7lRscv/rvhuK7Rlpm+79ym8ZDdWUS9mer3uF584My+IafvCPPszI2Q18sko7XdURpOuBTKNkeBw+yOkspqVQBqzvO/kep5doB7qR4R+EBrfXzNq/WPgaOAuTWv0ncCG/bj7r3Aa8B4qhfvLQK615xZMI3qgvA60A+YTvUpgkv2+Pfe3T4eCdwEYGnruK+3fu2oA+GwtsMIRmPWwi2pv+VxfTq2e3NOGdimIt3rOpf/PZNENDFpHvOGvm0z10wa6uzTA6tiCTYXBy3d90xnPd7XfOonGR9td4xU1uAXA1JKmYC7pgzYoixaNvO+Wfcd8+km52z09veRf09WlvQw73x/hd1RbNM6y8cbVxweTve6RvHfaSXRtLULx5JLr399Uda8TWV2Z7HNmYPb8H9HmFo9fqhzBkS6joIznl9IWu6gfX+yMzX4anCtddLOgz+g/C7/oXML5toYoWH5XX6GtB5iPvnleruj2MZlKB4+u1/Qbap7kIO/k2z1e8zxD4/vF26e7tzp4HcWbEdntlU062p3lIazeTZ40nsDDjsFYv858XSwLqF4SBdHiu3O0WBGth9JcTBsFVQ6d8v0a0d2jrXL8c/1uswH7c4iGtznHpfxyCNn9w8aznn9+z8sDasKQpbuf45zpgFiQdi1Mkz1qePiZzixABy+pGiJo07+P7P7mcmvVlU68f8agCM75zL+kHZV6V7XeOSyvo7kc5t3dmyetub0QW0c+/8/ee5Og0EX2B2jYa36OJ145AS7Y6Qqxx0Uwonw0fMK5jnm/P8sbxb9W/Q3//HtJruj2MLrMrjn1F4hv8c8D2gaV30SByOZ7nVd/Jvjukaz09x2Z7HFJ0sLsTyZ0Hqg3VEazoYZJonwOLtjpCrHFYBoMnrM0qKljhkIHN1hNMXBSKK4ypkXS7tsWMe4z2VORy7tK6pPF37xt6O7OvYqWEt2RtADJjhnFGTrD+BO6whk2R0lFTmtALgCrkD35cXL7c7RYE7telpy5soKR+6Gkt8sjfOPaB9P97mutDuLSA1pHvO2kT3zIv3bZdodxRYvzNqqGHCOgXLIU38yDiXrw1SfLi724JBHwY/6FEeKo1XxKrtzNAi/y0/vZr3MV2ZttjuKLf5wUo+gaag7qb5MtBAAFX6Pec3dp/QOmsoxA4E/+mZtCZYytaOmAbYv8CAF4Gc5rQActrBwoWO+58NbHU5ZOJQsqHDe6v+juuTSs1VGmds0/mZ3FpFyXm8W8Cw7+7C2zhkK383msoSlOw93ztkAOxb5iVYeYneMVOSYgyFAVaxq2PzC+QG7czSUUR1GJVdsiznq/xjAVIrbxvYIBryuq4G43XlEytHpPtfEa0Z0jjpxb4Av1pSb9BjrnPJTuByScdkS+Gc46uBgaWvo0qKldsdoMMe2H268v7jAceOcpw9uozN97qVUXxBKiJ+zQqGeu/LYTnZuSmaLt+Zug9b9TUyHlJ/C5eBJ68oe14cRzioAfr/b32FVySq7czSIDhkd8Jk+NWOls858cxmKq4d3Dmf4XNcge/2LX+D3mPeP7ddKO20UYFdVjFgkbNHOIS+Kg0WQjBuAg66GtH+cVAAGbq3cGoxZzjgd7sg2R1JUFU047Qh4Qp+WGAZLgHl2ZxEpr9DSvDjp6HzHLZJZtiuudJcRznl62LUqiiwE/AknFYDBi3YtckzVH9F+ZGLehqDjTv+7bFjHqkyf+x67c4jGIc1j3n/aoDY6x2GbA328vFjRfazdMRrO9gV+oK/dMVKNYwpAKB4asLp0td/uHA1BoRicN8g1dcF2u6M0qMM65tAs3VMKfGJ3FtFobE9a+o1zD2/nqEsGf7R4JzTvpvA4ZE30ziUewmWH2x0j1TimAEST0QEbyzfaHaNBdMzsiKW1Xra90u4oDerSoflVaR7zXmS/f3EAAl7XAxMOax/3uhzzdEg4YREOVVnkH2V3lIZRuAzQg+2OkWoc84j3mJ4uGyo22B2jQfRp3ofiYNRRB8FOzdPo1y7LMpSabHcW0eis1OgfTuzX0jlz4sDyIkvpLiOd8TxRuBI86fmAaXeUVOKUApDmNb1Z26ucMSQ+KG+wtb4g4agH+sVD8sOG4jHAcad1idrL8LnvvfTojkG7czSkGWtKFN3G2B2jYcSqIFIWBTrbHSWVOKUAdC8MFYYt7Yyye0jLwcxaX2J3jAaTnebm+D55yusy/253FtFofZGd5q7q1SrD7hwN5qMlBZDd3sDlsztKwyhYbiELAf+HU1aJH97SyM74/pTpVkmiQm+K7TTWlq1VS4uWMnfnXIoiRXbnqzMuw0WHjA7GtOWz7I7SYI7rlUc8aX3qc5uFdmcRjZZ2mWrymL4tf7NiZ6UjTgkoC8exoiFttuih2LHI7jj1b/uCNDoO7YdhvmN3lFThlAKQW/nRx8nyDz4wPfkd6NmpszWgR3drQtfjDPdRecpKJIhGglZpolxvjhUYa8vXqaVFS/lh5w/sCjeujXS6Z3cnGI/osnDCMbtendSvVUWGzy1z/6JWvC7z1ZP6t7r60WlrHVEAAIpCcatlXm/TEQWgYImLSPkQ0nLtTpIyHFEAkhUVfcJLl5qh778n9P33sPvUh1K48vLw5HcwPB3y6d65k9Wve/fk2Z2HG+4jWxo6mSQWCVpliUprU6zAXFu+Vi0rXsbcnXPZGdpp2/e0N31b9KW4KmHhkMUuWX4XvVpneJFT/0TtLfa4jLK+bTIDS7dX2J2lQSwvssy8Vv20WvRa03/BULgSDLOP3TFSiSMKgE4me8S3btnLX2oSBQUkCgoIzfkBqsvBjwXBldcCd4d8w5Ofb3Tt1FH36d49Ob7jMcp9RCsDyyIaCVrl8QprU7zQXF++/sdysC1ozxVoD8k7JLlqe9QRB3+AY7u3IBJPzvS4DEct4BL1QntM4+UT+7W8aen2CkdsGrZ0WyXDux2iccI++WWbweXLsztGKnFEAVBud35868EdkBOFu0gU7iI8dy5U/5L8eHA1mzfH06GD4emYb3Tp2FH37t49cUanowz3YbcboImFg1Z5otLaHC90rS/fwLLiZcwtmMuWyr2UkTrQr0V/9Y+lzlkAOLZfy8pMv/sVu3OIpsHjMv51Yt9Wv3no0zUeJ5wT+N36Yq4d0t0Zi8GjFYAygQzAWZuk7IUTCoDL8PubxbfV/SvyZFER4aIiwvPnQ3U5+PHnaebm4snPNzz5HYxOHTvqnj16JE/rdIThPuT/DJQiFq6yKuJV1pbELnNdxXq1vHg58wrmsbFi40HnUShapbU0Zq/fVOvvrTEIeEwGts/2Ah/anUU0GctMQxX1b5fVYdHWcruz1LvVO6vAkw6+bIiU2R2n/oVLwmS0agM446pw++CEAtDGqqqK6ng8rSG/aLKkhHBJCeEFC2DPcpCTgyc/33B36GB06Jivu/XokTil00WGZ9AtBoZBLBK0KuNV1tb4LnNd5Qa1vHg5CwoWsLZ87S9+zVaBVsStBIWVzri2ydBuzQnHk997XEbTf6YWDcbnNl46sV/LWxZtLffanaW+aSAWCVrevJ4Gm2fbHaf+Ve6wpAD8lxMKQItkWVlK7fOdLC0lXFpKeOFC2LMcZGfj7tDB8HToYLTr2JEuPbonxnU+z3APuMlQLhfxcE05SOwy11duVCuKVzCvcB5rStfQMasjwVg0iUMWAJ7Yt2VVlt/9kt05RNPiNo33h3ZtdgPQ5AsAQEEwqTvk9cYRBaB8q0GbQW3sjpEqHFEAEiUljWY6L1lWRrKsjMjixf+56cf/IyMrC0/79oanY77RNj9fd+7ePTG289mGu99vDOXxkAyHKIw3/bU8/3Fox2wX8KndOUSTs7hFuteT7jWpiibtzlLvVhdbZvtWAyzlhI3hyjb7ACkANZxQAJondu1qEq+IrfJyIuXlRJYuhT1GDoyMDFrdc7e1odthTeJ73Zc22T4UKgLYc7qFaMriwVhyZZ82mQO+31Bqd5Z6t2RbOaP6OuQ6ORXb3MSCHR1zFcR9cEIBaJEo3NXkT+mxKisxc3P1moIqu6M0iD5tMokmrAV+jyP6jmhgfrcxbUD7rH7fbyht8q+KF2+tgGHdms736fJBeh4EWlS/1byvM9skaXuYspLJI+VZo1qTLwA6Hm+ZKCpq8gUAwNOunbHsO2ecAtivbWY8w+eabncO0TR53eY3h3XMufzZrzZm2p2lvq0pqAJPAAwXWCm1XOq/fNkQaP4/B3ad3hIy2ybJbKtJb2kQyFX4shWGiY5HSCTiVjRu6YqY1rvCmIVVCVOXwpHZPm+W3d9PimjyBcAKh9slS51xUHS1aKGWbttod4wGMahDdsg01A925xBN1uzerTO9iuqV8k1ZKJ6ERKT64FrRQFdMNUxIawaBmgN6+n8O6q00We2TOqO1UuktFP4cA18mWEmsREzH43EdjltWWRQKQrgKK6Pmjq0RtpSE2FSykbUFVeyo+PGCoD8Z1ejRMoO3rhwix/8aTb4A6GSydbKk6c/jGYEAoBxxCqACurYI+IG5dmcRTdZOS+uK/GZpLTYWh+zOUu8S8bjlzmhl1KoAuHz/czAnkAfpeeiM1kky22kyWikCzQ38OQq3H5IxkvGYjiUSVjCmdXEEVRCyzIKKqGv7jjCbSyvZsGsH63YFqYgkoPpXX1GLxYolwRhu05ACUKPJFwBQeYmSpj8CYObmYsVijtjSs2PzNBKWLvNCsd1ZGPYpdgAAIABJREFURNOVsPTsfm0zT3ZCAYjEk5Y7o/VPD6y+rJ/OpQfyIKtdkozWkNFKkdZM4ctSmC50PEIyEbcicUtXVg+9GwVVCXNnaYSt60NsKSlk3a71rN9VRaL66uz/s7tqfSsNxfC4jADVJeLH68MrpTpqrTfu9rEBHAvM0FrXahBIKXUKME1rHdrjdrfWOl6b+97L1+ustV6/P5/b5AuAcpnNkqVNfwTAzMkhHo87ogD0bpNJwtIy/C/qVZbf/dX/s3fe4VFU3R//npnZvumVkkDoIB3poiCIAqIgiiK+ivqiqNgRxIq9v1h/dkUQRMQKitgQAekt9N4JkJC+fWfO74/ZSESQkLKz2ZnP8+RhZ3Jn7tmwO+fcc09pWTf24jnZR6I+hqg0KJDz/PHM3e9UyJFCsCeGXO8KOOhjfyDAnqCiFHqBY25IR0v9Ys4hDw4WuLH3+F7szi3FocLTu94jhaDCCMhKUBTEWACFAEBEEwDsALC33NDWAB5j5gVVmY+IRACPANhORP0BpADIApABYCaAt8uN/QXAxcwsE9E1AGKY+f2T7tcEQFMANgBJANIBNAaQCuAqZnYBGExEucw840zyaWIAENF5zLw4LHOZTHGyDjwAUmIifAEl2rcrAQAt0pyBOJspLJ8fA12zv3681Qcg6g2Ao24SC6Qs/LzjqLg/vxR78o5h57GSanO9RxLegBy0msQYAIVE1BBABjO/cNKwSwF8VA3TDQLwB4D2ABIAfAzgTWa+oPwgIuoGYBfUv7Ec+gmGflf+724GEA8gH0BXqF7Qe5i5kIgEIhKY+TUimk5Ec5j5X3sehMUAIKKfmfmicqeeA9ArDFNLZDJZ5aLorxQrJiagNKgL/Y+MRLsHf7fWDQxqgoN14qzKmYfVfo6W+FDk8cuTf9kR9RlynoAsx6sNgQDgPwDeAgAi6gjgf1AVbzsA24joBpzoEKsAeJSZl5zFdPcB+D10z33MvJuITvWZuh3AbwBmE1EcQvqRiG4EEABwD4A0AONC9wKAzJBM5xMRoOrzyVCLo30KYAiAaf8mXI0aAETUFkAHAPWI6PrQaQcA7+mvqlacHAgEwRz1FrwYn4Djfn2UAK4Xb2UANddS0cBA5WByjMWktRDhoMDlR504iy5WEC6frOCEAdCYmbcAADOvAdCbiDIAvMvMAwGAiMYDKGLmd89mHiIagnJxBgDuD7n2OxLRjwDsAK4FEAdgIIDfmPlyIro49LsggNeZeXbo+g1EtADq1sE2qEZJEEA2gGYARpaLKVgG4FFoaQDgxH50+X+PAxhew/OWYYIs68KCl1KSlcIAR4WL7kykqA9lwwAwqGlynBbJKhJBrlocWMRT4A6gRR1n1McPAYDLFwROGACnYgyAN8sdDwRwdSWm2grgFQCdQ8evMPMUIpoN4PqyoMCQ+/+50GsBwIMApkM1Hu4ioh/KxobiA/pA3U6oFxpjB9DhpIBCD9Q4gX+lRg0AZl4PYD0RNWdmLXq2m1kvBkBamlLkCUS9ASAQ4LBIVgA5WstiEPUE/EGlJMlpjov29NpCtx9OS9THhAMA3H4ZOKEcPUTkZObyJVQLADxIROdAVeK7mfno2c7DzFuJKLn8OVJ99VugBhmuCI2bTURXQt3bfw/AT1BLnDsBvADgByK6gZn3ha4vgNoCvQdUD8AKAE1OyirIQgUWSeFSGK8S0TVEdH3ZT5jmNXFQjm7TPYQQE1Nm2UY1sTYTArLigbovZmBQo/hl5WhabPQ3Bcx3B2A3S1G/gAAAT0AmnDAAfgAwrPzvmfllABdC1Y9fA7AQUb1qmLo+gPcBLABwBRHZiah8U4I+ULfHGwB4HMADAEZAjUvIDI0xARgLYCOAwwAOhl7fir+nUw6HaiT8K+Ey+X4E8BXC77Y1QdZHZByZzPD4o79zWbzNhIDMRVZd7MwaaI3CfCA11tIs2ltOFboCsOhD/8PtDwo4YQDMBfA5Ec1j5mNEZAHQEcBQqJH77aGm7H1FREsAPAQ1/e4BZr6nAtMJULe+UwBcB1UP/glgEoDRULcing6N/Tm0RWABcEVIxqnMXH5lNwJq4GIwJJcCoG1onlYAJhNRMwD1mDkbZyBcBkAJMz995mHVjomDejEATOTxRv9uR7zdBFnh6C/sYBARmEVhd2qMpa/WctQ0+S4/LJKoixgAj18Woe6bg5mZiO4D0A/ADABPQVWm3zLz+NAlG4noJwBXM7OXiI4A2FPB6SyhnwUAvgNwPtTo/JuguvfvKzeWQjL5Qq5+c3nlT0RdoCr/siB6P1QDoGzMQCJaA6Al1GyBMxIuA2AREX0GYCoAFwAw8x9hmFdHBoAETyD6PQBxNhMAztNaDgN9YBKFYosU/ck1xd4gdLIDgKDMf6tpwMwHoSp/lFP6f4OZ5bIxISqUEcDMPwP4udypz8q9HlZu3Oxy53GqIj7MvAKqoXImFlZENiB8BkAAajBFZ6hWDkMtjlDTmPVjAJhID1sAokBgY//fIEyIAvn0sDD2BxUI0f82AaAso6PS1k7IGIiKh224DIC9UJV+uJtrmTgQ/YFxAECSCb6gX2sxahxZLXZoRAAYhAVRoICkA80YVBSEislEPQpz1FQ1rCrhzPsoi7y8BEAe1O2AmsaEoD4Wi2SSwBz9BoDCDNJBDwuDiCHmyrbJSt/GsdHtSSQCWBF/uLNHVKxs/43UGKsEIPmMA3VAWB6kzPxJucN3iOj/wjEv1BiAME2lLSRJqGLTqlqBrDA4jN3DDHSPz7F/lSCu/lFrOWoUMpkhjHwS+7KXRf13y3nOOQpiMz1ayxEJhKsXwPnlDlOhpiuEAxMH9OEBAAiCDlx4oS0AwwNgEC58/u0r4J739plH1mYcCXCMmITly5drLUmNk5KS4s/MzCzWWo5IIFwP0j44sffvB3BHmOYNkEkf28WKz8tWc9Qb75AVYwvAIHywooiQo9+LKJjMuvAgAoAgCIwTqXO6JlyBEM8COAogEer+/7Ywzesi6xnLIUcF7PHAZor+uBbVA0DRb+kYRAay38k6CK6FyaonA0BBlETxV5VwaYyPoLYynAe1gcHHYZrXLdis0e8XB6C4PbCaol8vykYQoEEYYZ8nQynO1VqMmkeSwBz9hcQAQBRFwPAAAAjfgzSDmf8Tej2fiCpcqKCKuMli0YcB4HHDakrVWowaxxtQQAIcZx5pYFANKEpdpSj6604JJitY0Y0HgGF4AACEzwA4TEQTASwH0B0IW2Vtt2CxRP+yGIDiciMmLfoXxjlFXlglMRXhrylhoEcEIU0pOqa1FDWOkFgXPr+/rFZLVGPEAJwgXFsAY6AaG1cCKIbauSgcuMmsg8g4AIqrFLHW6DcASrxBMLMAtXWmgUHNIpkT5cLoNwCk9EZwu1y6MKgNA+AE4TIAPgWwn5lvh9r96KMwzesmkyn6tSIApbSUnFZJF1/gvFK/F0BDreUwiHqIzNZYPcQAiCmZKC0t1cXzw2w2MwCf1nJEAuEyABLKigEx87MIXxUmPwSBIEa/E0ApKaU4i6iLKJ5DhR5A7ZltYFCTxCPolxGIfl0hJtVDcUmJ1mKEBYfDAQDR79apAOEyAA4S0QQi6kNE4xG+Pz5zMBgQrNYwTacdwfx8ihP1kcez97jbAsMDYFDzNJCLcr1nHlb7obhUFBcXR/9KCYDD4RBhGAAAwmcAjALghhoD4AFwQ5jmBWTZR7borwUg5x+HUx87ADhY4LF4/HJTreUwiHraBPdkR39xDQAcmxJ0uVxaixEWLBaLBYYBACB8vQB8AN4Ix1z/mFuWfYLVGvU5H8G847CIgi4eVocLvfAF5RY2fcR3GmgE+70d/bvW6CLllG2x0IMBYDKZQGrbw1KtZYkEol9hKIpHsNu1lqLGCeblwWQ2RX0KDwAcLvRAEKix1nIYRDfsc3UL7tuoi+8UmW2CHgwAh8OBYDBYBCOFGMBZGgBE1PCkYyG0r1+tXxIiMlfh2kblj1mW88SkpKoLFeEEjx6FaLWQqIPH1a5cF6ySWBdqRomBQc1gsrYM7t+ktRRhgUxm0osBoChKgdZyRAoVNgCIaAKAjiedbg3gMa6GItJE9AgR9QsdfkVE+UT0IxHlEFFmaEwTIhpARFcQ0WgiepSIphDRD0RU5qobTETXlrtxjqQDA4B9Pig+PxqlOLUWpcYJyIzduS43gG5ay2IQtSSRINjkvANayxEWJMmkGwOAmY39/xAVMgBCK/8MZv7qpF9diirm9BORSER/Qg0UfJmI7gcwGMAGZr4EavXAQiISAJihFoBxAWgCNZfzHmYeCMBDRAIzvwZgEBHFAIBgNu+XkqPfAACA4PHjSss60W8AAMDS3fmOgKycf+aRBgaVom3w8E599Iy3xkAQRXi90Z/w4HA4QESHtZYjUqhoEOB/ALwFAETUEcD/oFZSagdgGxHdANWYEAAoAB5l5iUVuTEzywB6ENE4AKuY+XciugLAstCQOKgBGxcCGIcTFZwyQ3OdH9qBkABMBjAfauGhIQCmCXb7ASklRQYQ9RFjwSM5SpPU9OiP6wCwel+BNLRD3QHxduFRrWUxiD44GOjt2/RH9AcPATA1aAWf16uLboB2ux0mk0kfbp0KUFEDoDEzbwEAZl4DoDcRZQB4N7T6Rii/v4iZ3z0bAYjoHAAPQu0SeDkRbQLQCsBlRPQmgGWstqn6hYgWAJgJtZ2wANUYyAbQDMBIZg6EbrsMwKMApgE4KqWne4HobyDj3bJVaNSzkS7qeWcfLIbDLLYGYAIQONN4A4Ozgb2lQ33rfjFpLUc4MLfsifyCfF0skmJiYgKSJBkegBBVSQMcA+DNcscDAVx9tjdh5k1ENAvqQ7wFM79KRP8FMAdqxyYLEcUwcwkzy0TUB0ACVINBAWAH0KGc8gfUWgNlyf9HTHXq6KLus2/7dqFB/0t18UUu9QVxpNjry0i0d4S6TWRgUF3EksXe3L/lT63lCAvmJufiyJGjWosRFmJiYnwwagD8RUXdxR4iOnlzuQDAg0T0ABENBrCbmSv7KXoM6mr+EiK6EUAi1G2E3gD2lQ0KZRsUAJgLYAOA9aHXJURU3lrPAlDm5jkopaVF/YoYAPy7dyHRKujivQLA8j0FZmY+T2s5DKKO3oE9673w6yMEAPVaBI8ePRr1iwYAiImJkQFEf3OHClJRA+AHAMPKn2Dml6HuywsAvoa6Uq93tgIQkQR1P/9GAD8y88dQV7CvEtHvoTnKPpwmAGMBbARwGMDB0Otb8fdV73CohgEAHJCSkqK/FjAA367dsDgcuogBAIBV+wqsxd7gIK3lMIguFK9rkHfl9/qIpgXAsSlCXl6e1mKEBYfDQTA8AH9R0S2AuQA+J6J5zHyMiCxQUwKHAmgf+smAmr63BMBDAJIAPMDM95zh3l0BvAKgLYB+RPQ1Mz8H4LmyAURUpsBHQA1IDIbmU0LXCVDjBiYTUTMA9Zg5O3RNIYkiBIcDSpSnuSjFxWCfFy3TndhyJPoLXa3YUwCbSewOdRvIrbU8BlGCIg/yZf+mD0NaECFZbboxAOx2uwTDAPiLCn3IQ3n+9wEoy9N/CqpHYA4z92fmjcw8D0APqJH8XgBHAOypwO1bQ/Uw/Adq9P77RLQ+9LOdiHIBXE9EXUJjvFANAH/op+z1QCK6ACeyBf4SX/F6j0tpaRV5q7Ue3759crdG+kh7LHQHsCWnxA9ggNayGEQNjQFKCu7NPvPIKMDUpBMAQnFxsdaihAWLxWKFsQXwFxUOAmTmgwBmhF6PP80YuWxMiDNmBJyUNTAz9HM6+v3L78pY+I85gsHDpvT0Ov7duytwee3Gt3kz2rbro4tMAACYsz4ntlGK/cYYq+lLrWUxqP1wwHeDZ/Esgg5S4gDA0qE/8vJygwhTXxgtsVqtUBQlIAhC9Bc8qCA15uZiZjnkCdAcEsUdpoz6WosRFnxbt4lN403R3vvoL/yyArtZvARArNayGNR6iIP+0Z4F0yxaCxIuTE27KAcOHNTFYiExMRGBQOCQ1nJEElFv9QGAGBu72tK02RVQKwlGNZ6NG1HPGt1dAc+pG4Mx5zfkLnUsLHBQQMERIKnupfi798nA4Gzpzq4iZ2DXGq3lCBtct5l8ePEKXdQ7SE5OBjPro7lDBdGFAQBgi7VlSw90YAB4t2yBZLcJcTYJRZ7oKX9QL96KMRdkoU8Dq2K1mAXv8u+U4unTRP+WJbD2GCbG3fzKWMGZYBgABpVG8ZSMdv/yke3MI6MHcsRLR44c0VqMsJCSkiJbrdbVWssRSejFANhsbtxIF1YuAgH4d+9WBrROF2auPKi1NFUi1irhv70aYlCzGDneaRN9GxfK7vemiEVrfwaC/r/SPr0r5iJuzJsdoRaHMlx8BpXBQqJ0leePz3WRDw8AFJsCk9lCubn6iImrU6eOSxCELWXHRNSQmffW9LxEZGZmfyWvbcTMNRa8phcDYJ/ocJgEhx2KK/qzxVxLl6HXeQN55srat7dnFgSM6Fofw9smymnxdjGwN1v2fPaseHTFXLCn+NQP54AX3kWz2Hb+NXeQ2fpQmEU2iA4GB/ZvVvTS/Q8AbOddheN5eYosy1G9ZVhGSkoKAdgC/NXddgeAvUT0DoDOUIvM1QFgAbAXasXZlcw85mznIqJHoJax/wVqenwPACug9s/pysz7iagJgKZQq9YmAUgH0BhAKoCrmNkFtbttLjPXiHdTLwaAorjdB8xZjRp5N27UWpYax7N6tdDq0stqVUngS9uk4z+d0+XGyVZRzjuoeOa9JOQumQ2lsGIVykrnvG619Rp+J4BnoHaLNDCoKKS4ih5zzXk9RmtBwom562W8ftu2WrdIqAyCIMDpdNoB7CjX3faF0K+9AO4PNaIbBaA+Mz9NRL0BXHk28xCRCGARVCV+JRFNg9rd9ndmvoSIvsE/u9vmQ62Hcxxqd9tCIhLKutsS0XQimsPMJVX7K/wTvRgAAPNGS+PG+jAA1q1D3Ri7WNaaMVLpkpWAW3pmcNs0C8jngvu3Dynvj88g5+w86xWJnLMTvk2LydK+700kiG/UhLwGUct57HM18i77Vms5wkv9c3j38q91sfpPSEhAMBg8LoqiF+W625bjFSL6ywMQUv4JAJaezTw13d32bGSpCLoxAITY2FWWpk0HQgfvOZibC6W0lM9rlkR/bD+utTh/o0mKA2N6Z3HPehY2iRA8i79QCt+bLgZ2rQaqWLug9MsXHeaWPR4hm/NtnPhiGRj8K4qr8OnS2c/bwZFsLlcvQlI9mGwO4cABfWx5JCcnQ5bl7aHDv7rbhpAAfAhgDYBBAFIATIFa7bb12cwThu621UrUK8MySBA2W1u1dEMn+eLuNWvli1q2liLBAEhxmnHrBVno38gu221W0bt6nlI6e6ro37gQUORq26YI7FgB+cgum5DV7goAs6rrvgZRTVswOrsXfqYLV3gZ9n43IScnR5bl6vv+RTJpaWmK2WxeeZpfvwVVYTuhuuKnhF5vB7DgbOYJQ3fbakU3BgCALebGjXXzJS9d8JvU5Z5OmlX4skoCbj6vIYa0ipMTY+2ib+tS2fPxFPHo6nmA31NjD52SL56LiR/77hOCPe4LAPoo52ZQaRR38aTS7yabEfBpLUpYkToNlLdt26YL9z8A1K9f3yVJUpkB4CEiJzOXElEcgPegKmcAOBeAA6oCB1Tle7alxh8DMBFqd9sinOhu+zsR/VXp9qTutj2gegBWAGhCRKZyRkD57rbVip4MgJ1SUpKNzGawv1IZGbUK16LFaPz445JJIASU8OhBAcDwzvUxon2yXC/RLgYPblM8X04Wjy77FlyaH5aVhm/1PCiuogzBHjcAao8JA4PTkQVBHOCe/6EuVsHlobQsYfdPS3SzIKpTpw4BWBc6LOtu+wkzFxFRH2YOElEigO+ZuWfZdSEXfoU5VXdbIpoItbttIdTtgJO728oAGkLtaVO+u22ZATAcasfdakdPBoBf8XiOmhs2rOfbvv3Mo2s5wdxcBI4c4cva16Ev1xyu0bn6tUzBqK51leYpVoGLctn96+tC7qJZUI4fDP8KgxnFH41zxN/14duCLaYJTnyJDAz+huIuftX1w9sSe/TRCKcMKasdRFGinJwcrUUJC2azGXa73QLVpQ+c1N02pPwzALwP4H9l14Xa2x8u9zoSuttWK3oyAABFWWlr21YXBgAAlMyfz4P7D+Mv1xyudkXcrn4cbumVyefWsUCQ/eT5/VPkL5yB4IEtmq8qfKt/RHDfpiRT03NvJVF6U2t5DCKSXhz09yv95n/6egYCsF90I/Yf2B9kZl2897S0NPj9/t02m00G1O62RHQfVAW9CMBnUFPwXmLmXwEglAXwGtRIfODsu9u+BOBqqN1ty1rR2qDu9z9MROtworstoK7+FZwIXh5IRGsAtMTfu9tWK7r4AJQhxsX9bO/cuX/h7Nl2rWUJB6ULfheaX31NtYU2ZybaMOb8LL6ggZUtZknw/Pm1UjT1UzGwbRnAHFH7iUUf3OtIfubXZyFK06HusxkYlCEq7pIPiz96wA6/R2tZwo7Y+sLgtjWbdfPsT09PBxH9LQCwfHdbIurLzCcHgSwC0PmkCn4R0d22OtHNhyDEUnvnc3XTKc+TnQ3JJAkt02Ow5UjlakjE2yXc0isLA5o65ViHTfSu/1XxvP2JWLjuV0AOROzeaXD/Jnj+/NJk7X7FU4LVMVZreQwiB5blm4M5O+t6/9RhB2kiCAl1pN2752otSdioV6+ex2q1njaf/xTKvyyfXz7dcbSgNwNgg5ScbBZiY6EU62DfT5bhWrpMHtGlpfjYd1vOPD6EWRBwfY8MDGuTKKfG2kT/7rWy59OPxaMr54K9pRGr9E+mZMYkq7X7FTcBeB0n9v8M9E08gr6Xit67y6G1IFpg6TYUgUCA8/LyNN+qCxf16tXz40QAoEE59GYABBW3e6OtXbtOrkWLtJYlLJTMny/2HN+xQmWBh7avg5Gd0uSGSTZRPrZXcX/3rHBs6VdQinJrjdIvj1KUi9LZz5udVzwwVbDH9kBkF0Y0CAOK1/WMd9k3puCe9VqLogm2AWOUtdnZQBWLbtUWTCYTEhIS7AD0+R9+BiJq3zYckN3+i71jh6hz5ZyO0gULEBdjE1Ocp+6E3LNxIj6+vp2yclwXntgjllMWv0N547oh7/6ugvvHd0kpqt2dwlxz3xTlo3tbc8B3t9ayGGhObwQDN5ZMe0RXLX//gghCw/a0YcMG3Tz3MzMzEQgEtsDoD3JKdPNBKEMwmxfbu3Ur1VqOcKGUlMC9Zo08+vysv861SHfi1eGtedm9nZTXLmuIxpu/4PxJAyn3tpZUOvt5QT5akWDXWoIio+B/1ztYDj4DNaLWQJ/EK17XF4Wv32xTSrSvjqkFtj7/gT/gh17S/wCgcePGAbPZrLMmDxVHb1sAALDM2rKlDYIAKPrwCBfNni1e/NCjsnlwC7Fvlk22WS2id8VcpeTzaaJ/0yKAlVrp4q8o8tHdKJn2iCXmuie/EmwxbWHUBtAdirvkI8+SL5y+dT9rLYpmWAfcHly1PluATtz/ANCsWTOPKIr6/U8/A3o0API4EMizNGlSN9rrAQhOJ5JvGwP7pUNkyekUL7Ztk90fvCEWr5kPBHxRrfRPxv3zh4K1+5AMc9POk8hif1hreQzCByvKCMVV0L/4k4nWM4+OUsw2UFojqak1STl+/Di2bNkCr9d75utqMTabDfHx8RYAy7WWJVLR3RZAiCW2Du21lqFmkCQk3ngjGsybLzf+cxmCF14pL51/TDy8o1CW83ME7/LvoLe652UUvv5fB8vBewF001oWg7CRiYD3vYKXr3PoMee/DOfl90B0s2LfGhD6n99XHjduHEaOHCm3bNkSkhSd68CGDRvC7/evhlpkx+AUROf//BkQY2J+sXftOrDw81lRkwoUe+kgxI+6STY3bSa6Cn3K2sWHhR3vLoOrUF3plxb6xItv7K9AR+6/k1EKj6LwjdG2+Hs+/l6w2NsCOKS1TAY1illxl3zr+nayNbhH31lgtt6jgqXLcsTSPw4B8yAK8RbUuShTvGzgYFm0SOLWrVvltWvXinv37gVzdPTQaty4sc9qtX6jtRyRjC4NAACLHN1q/yLQ1qULkm67XbG07UBBmbHlzxzaNnsVCo64/+HZObi1ABBMgqXTAPhWz9NC3IjAt3oeXF+/Euu4/J6fBFvMuVC7fRlEH6R4Sj/wb1vWrPSbV/T6nAMACMn1IcanSu61q/46pxT6UPjFDgAQTXUdaNIvU2h2VVOFCUL2+vXyuvXrxdoeLNi0aVO/IAi/aC1HJKPXL8ZWwWLxmBs3dvh37dJalrPC3LgRku+8iy3de7FgNgs7Vh3lLW9mC0f3FAP/srpnhbF1WY7SfOgE+FbP0+vWDwCg9KuXJKlhmyxLu76fCbaYoTDaBkcdHPDdqRTnDiucfIMdUbKirSwxIyax/0CJrJT4T/m8Dxx2IX/qFgJAlhaJaHNBC2o/qgN7vV6sWbOGs7OzhYKC2lVNOyYmBna7XYCR//+v6NUAYAbmOC+4YFT+rl0R7xIXU5KRfMcdsPW9RDbFxYj7Nh5XNk/dLh7cUgBF4QoH821adFhoNfFcwGyDnvdDAaDwjVtsyc8t6EfpjR4ls+1JreUxqFYGst/7fP5Tl9vYq5uM39Ni6XgpF8zaXaFnvW9rPnxb8wUAsHdOQ7funZTzzjsPBfkFyqrVq2jTpk3kckV+Sn1WVhaCweBik8mkj1SvSkLRst9TCQZ7srM/3Tv86litBTklNhuS/3sznJcPk01pKeLR3cXypkWHxT3ZuQj6K/+ZHja+k+LcOkMomTKhGoWtnQgJ6Uh5eZlHiEkciRrqt20QdjooPvei/CcvcwR2rNBaFs2xdh+K2P++j5xnl1fezyUJiDm/Hswdk2RTvE08dOiQvHr1anHr1q3w+yP4PFK5AAAgAElEQVQzvm7YsGHuNm3ajAPwttayRDJ6NgAcit+fv6N7D7MSKRatICD+6uGIu/Y/srlBA7HwqFvZtPgw7Vx1jLyu6kldz2qXjL7XNVbyb66j622AMkyNOiDx8e/dgs05BICRL1y7aax4XSuK/m9MgnfZtxHv2QsHya+sVVwbBSpdeLBa/h6CXUJMv0xIreJlyWEWd+3aJa9Zs0bcuXMnlAiqqzJ+/Hi33W7vAKMHyL+i1y0AAHCx273W0b1715JftI0Tcfbti8T/jlbMLVoJXneQsxcfpu1TlqPkuLfalfTeDcehoAXZLrwBnt8+qe7b1zoCu9ci/9kr7IkPf/WNYHUOBvCb1jIZVIrGite1rGTaw/GG8lcR6jSBlN5QcH9cfZ4QxR1E0Xe7ge8gSsk2ZPTLFBpcNkQRTKKwadMmed26deKBAwc0zSRISkqCJEleADs0E6KWoGcPAFhR7in69rtncyZODHttcGu7dki+43Y2d+zCDEHYuuyIsnXpEeH4oZrfs+x4cabS8Tynkn9HMz0bgH/D3LIHEiZ+6RasjoGo4R7cBtVOE8XrWlYy9aF49y8f66rA1b+R8OhcRZFbcP7MbTX+NzE3jIHzwkxFzLBTUJFp3dq1yrr164Xc3PD3Ejn33HPRr1+/L6xW6/CwT17L0LUBAKCZXFS0dnvXbvZwTCZlZCBl7B2wnt9XEe1WYdeaXHnLnzni4Z2FYY1Dt9gl3PB8TxQ/cyn8W5aEb+IIx3xOLyRMmOUSrI4BAPTRLrL201TxupYWf/JggufXT4xtrRBkjUHq+/uQ+94GBMKwqCiPrU0y7L3qKGKaXSh1lfLq1auxYcMGKg5TC/Zrr722pFmzZmMBTA3LhLUYvRsAJJe6cvaNHJnm27atRiYQ4uORPGYM7AMulU2J8eLBLQXy5iWHxX2bjkMJave3v+Da5sGmmcXIH9/V8AKUw9z6AiSMn2kYAbWDZorXtbR4yoR4z29TDeVfjrg73mGp6WXKsdfXaucREQBn97qwdEmVpUSrmJubq6xevVrYvHkzPJ6ayUIiIkycONFnNpsbwyj0dUb0bgBAcbney3v33ZuPv/d+9T1AzGYkjboBzmHDZXPdOmLu/hJ58+LD4q61uQh4I6MTcWyyDSMe64L8cZ0gH9mttTgRhblNbySMm+Emi300CcIMreUxOCWdFa9rfvHH4+M8C6YZyr88oglpH+Vw/uc7yLs1X2tpVMwCYvtkwNQuMWiKtUr79u2T16xZI27btg3BYLDapsnIyMDIkSP3W63WBtV20yhG9wYAgAGeTZtm7h12ZZXTAWOHDkH89aNkc6PGYkm+V9m8KId2rDpK7uLITJW59I62wTRsooKnBhv7pichZbRE4qNz3ILNOZks9kdhFAuKGFhRrmG/58PC12+2+1b9oLU4EUfM9c/C0v1m5ehLqyLSMBJizYjtlwmxRaws2czitm3b5DVr1oh79+6tcibBwIEDfR07dnxBkqTHq0ncqMYwAACb4vcX7Dz/AotcWHjWFzt69kTirWPY3LotAn4Fm5fkYNvyI1R0LPIL7aRkxmDo/R1w/PZm4JI8rcWJOIS4VCQ+8o1LTG3wo2CLGQlAn12UIgdB8bmfZk/p3fnPDLEH923UWp7IgwipHx1RCr/bJ3jWhz8A72yR6tgR2zeThSwnQyQhOztbXrdunXj48OGzvhcRYfz48R6bzdYBQM3s6UYZhgEAQC4unnvs5ZcHFc76okLjzc2bI2XsWLZ07cmQJGH7iqPy1qU54rF9JTUsafUz+K52ckrpMqHw5RFG6tSpMNuQcPdHHvM5vbYK9tj+AAxLSRvsirtklnxkV+/854Y5lKLIV25a4Bg2HvaLx/GRF1ZSbfNZWZrGw9m7viLUs5PP78PatWuxfv16ys+v2DZGgwYNMGLEiN1Wq7VxDYsaNRgGgMpQz/r1n+y9+pqY0w2QUlORfOdY2C7sL0sxTnFPdq68eUmOeGhbIVipvX/D5PpOXPFARxy/NQvsLtJanMiECDHXTvLbL76lWLA6roSRJhhuGimeku99q39sUPh/t9kQjMwttUgg9cPDStH3BwX3utptINk6psLeI12WUu1iYVGhsnr1atq4cSOVlp4+o2Hw4MHe9u3bPy2K4jNhFLVWYxgAKlbF58vf1e8iW7Bc3qpgtyPp1lvhGDxENqUkiTk7CuXNS3LEPdl5kAORU/Wqqgy5r4OS6M2mwqcHG16Af8HSoT/i7/rAA9H0pmB1PAygesozGpwOYlm+mYO+V0tnPmV1ff+WEavyL9gH3gHH0MfV1X+0PJ4kwHlefVg6JcumeJuYk5Mjr1q9Sty6dSt8vhM7coIgYPz48R6r1doGQO3q8KYhhgEQQi4p+SL3tdeGFcz4jBKuG4nYq6+VzRkZYn6OS9m06LCwa80x+NzVF60aCSTVc6DvqGZyfKpNBAj5950LJXef1mJFNEJcCuLv+tBtatxpn2CPuRxGtbGaIlVxF3+qFB7tUfDKdY7ggS1ayxPZCAJSPzysFH67X/Bk1+7V/2mxSojrmwGpTYJsclrE3bt2y6vXrBZ37tyJzMxMDB8+fLvNZmuutZi1CcMAOMHAQInrW5gskrvEz5sWH8aOFUeptCD64r5ik63od2MLObm+Q9zw20/ysi8/Ey+88dZgw9QYKni4t7HKqgD2i29RYq970gvRdA9Jpg9gZAlUJ4MVr2ua+6cPbCUznzIbLv8zEzv6VTa1Gc7HXl0bkZH/1Y2QYEXcRZkQmsYookUSXC6XEhcX97AgCM9rLVttwjAATmAO+uXi+e9vtOzdcFxrWWoEe6wZ/W5soaQ3ihG2L10kL/58mliar77XmKQU3Dj5HRQ/exn8W5ZqLGntQKrfAgnjpruE+LRswR57I4zI46qSpnhKJrPXfXnB5Ovtga3G57AiUEwyUv9vO/I+2gT/vvBU24skTA1jkXpLWyaBWgHYqrU8tQldWIsVxM+MKSkNYqPLzw/AbJMwYMw5fN1TneEp2sZTHxiLH99+9S/lDwAlx3Ox4bf5Qecd70fd+68pgge3Ivf+ro7S2c93UbyutexzvwDAobVctRALB3wPss+z27Pg0yty7+loKP+zIOG+abJvT5GsR+UPAKLDBPYG18BQ/meNYQCUw2QR3z+nV92o8flLkoB+o1rwDc91gUCHlBkP34c5k58TC4/mnHL80tmfSXAmS7b+N4dZ0lqMHIRr7pti7t0dbN61P49VvK79rCjXw/huVQQCMETxlO71b17ySO74nvbiKRMs7Kl96bRaITXqCFPTbmLhnN263bpzdkkvFeymt7SWozZibAH8HfJ5gnu/f3N9Zs6u2psSRwLQa3gzNOuaxHn79/LvU98XjuysWFvsNhf25wtGjuL80Q0FyIYz4GwxNe2CuFteLRVTMg8K9thxAH6AER9wKtor7qK3lZKCNkXv3+PwZxtdmCtD8hubgt5dIhV9t0uXBoBgl1BnYlcvmYQ0APp0gVQBY5Xyd9hkFt5p2bOuV2tBKkvXyxvhppe6cXpWUPn2pado5mMPVFj5A8CGBT9T8fHjiLvtbUNpVYLAjhXIe6CHs+jdO1sEc3bNVNzFOwFcA8BouqSu+PsorqLFSmnBnyWfPdUl9+4OhvKvJLY+/4EYX0cq+WWfLpU/ANjapjAHlZ9gKP9KYXgA/kn9gE/e+eG4RZbalOvf/qIMdOxfV/G5imnBJ+/R7jUrK32vlAZZGPHUS8i/vwuUY3urT0gdYulwMWKueaRUTG9cSlb740TCJ9BfSWERwFDFVfQke0oyS2Y/b/f8MZOM6P4qIEpI/eCQUvzTQXItP6Lb+h1p959bYkqxXQVgvtay1EYMA+AUeF2BpX/M3N5tx8qjWotyRlr2rIOul2UoStBHCz/9kLYtXQRUw/9p35tuk5s3a0CFE3oYXqJqwNSiO2KGP1xqatIpSKL0JpksHwOI9jaMSczKNex1PSwf2xdTMusZp2/VD9Xy+dQ7cXd+wFKTgXzs9bWCXjeYzA1ikXzTOTmCRaoPRE3po7BiGACnZmjugZJPZj2z8rSlgbWmUYdknHdlliyaFHHRjCm8aeGvxFXspFUes82G0W9+BM+U++H9Y2a13VfvSA1aw97vJp/t/KsZcnCr4Ex4C8AXAGpv0MnfsQIYpLgKx5DJ2su3/tdg6ZzXHUZUf/Uh1mmK5JdXIH/6VkRMu18NSBzZ0m1rlfQIiTRZa1lqK1U2AIioITPvrR5x/nHvywD8wszuk86bmLnay7ASUSNm3g1ACvjkI1+9vDop78Dpa09rQf0WCbjg2kayzSmJf34xg9f//APJgZqpSNu8x/nof/NtfHxME4I/8rsb1ipEEywdLoL9optKLeecL3HAN19wxH0AYAEAl9binSUigJ6Ku2Q0SaYrAvs2yO6fP4rxLp8D9hhbs9VNwmvrFZ9gp5iEZCr+aa9SuuSw7rwAgtOEOhO6eMkk1AFw9m1cDQBU0QAgogkAdjDzV0T0DoDOAAoA1AFgAbAXQAKAlcw85izvLQJYCuB6AP0BpADIApABYCYzv11u7C8ALmZmmYiuARDDzO+fdL8mAJoCsAFIApAOoDGAVABXMbOLiO4GkMvMM+Sg8vDOVcce/mXKZtvZ/VVqhtSGMbjw+qZyTIJFXPHdbGXN998KAV/NxyqOePIlOSGYh8InBuk20KimIWcibD2Hsa33dSWmzHOs7C3NJnvslyRK8wGsR+S5NwlAMwD9lNKCIWS29ZDzD8vuX6c4PIu/EJTjh7SWL2qJufYJ4Pzr+cO7RlO9lq0weOyDiuAl5H++TQgcrm12Y+WJuTBDjjm//gzBKl2vtSy1mUobAETUEMA4Zh4bOn4VwDfM/DsRjQJQn5mfJqLeAK4sG3cW978MwPkAVgFoDmAagDeZeeBJ47oBuBHAWGYOENFVAJzM/DERld+/bgGgHYB8ABcCOA7gPWYuLBvHzAoRTQcwhpmtQb+8f8rEJVafS7t0uIR0O/re2FxOTLeJa+d/r6z4Zpbgc4Xvix6bkoYbXn4Trg/ugfePz8I2r14hqxPmc86DpcPFPmunSwKCM5E56PtNcMT/CtUY2ADVyA4nJqjfn46Kq2gQRKkvAl6rb92v8K79ye7fuBBKYeTHy9R2xLrNkPD8Inz1/BM4uGXjX+f733IXn9OrL7lW5MjF8/eJXIuClyuFANR5uJtbdJh6QP1OGFSSqhgAjwKYzcxbQsevAuiFU3sAllbCAPgdwO8ANkJV6FOIaC4zX3rSuKkAfgMwFEBcSAYAWAK1W9s9ANIAjANQpskzoa6qDoaOJQCTmXk+EQ0AkMzM03ye4KzV8/YOW/vT/rAHwjkTLLjophZKSqZT2LTwV3np7Bmiu0gbT1erC/py31GjkX9nG+KS6CyTHKmIKZkwt+kNc7MuXlPjTj6pbhM7BwPFCPo2kS1mCUnmbKif4yMAjgIoReXqDligetlSAGSxIrdid3FHAG3J6sxUio55A7vXkW/9r07fht8h5+ysrrdoUBGIkPjWluCODZsw/53X/pFSmlgvA1dOeEK22+OF/FnbyLcjer3i1nOSkHhls2zBJrXTWpbaTlUMgCnMPKrc8ZsANgNYA2AQ1AfJFAAdAbRm5tvP4t5DANwF4A+oBsDjAA6F7rUGgB3AtVAV/kKonogpRHQxgKegKvr/MfPscvcUAcyEWq+9Y2hMNlRX5siymAIiSgDwKDPfB6Czu9i/YMqExY5wxUpanRL6jWqp1G0aK+xYsVRePPMTsSRP++5eQ8Y/JtdLcnDB+O5GPruWEEFMzYKpwTmQGrZVTI3au8SkeooQlyIKjgQriBT2ewohB3LByAcgQzV2GQQGQwFBAcMGolSIpiQyWeIgiCb2lniV0oKgfHQvAnuz7cGDW6Xgoe0I7t8E9rnPIJhBTRJ7xzuK0ro/fXzvGPq3rb8uQ4ajx9AR7NtRqBR+s0tUXNHXsTrltnYllgaxtwIwXJJVpDoNgJYA6oUOx0NV/sdCx4fKPAUVvHcLqPvznfF3D8BsANeXBQUS0ZUAGkB1508F8CuAb6A+8K4CcEn5AEIiygOwLiSnAiAHQAdmTio3xgrVG3AbAPjcgezfpm1ts3ttzSphySqg739acoPWcbRvwzr5j08/FgtyImcv1eqMwU2vvovA3Nfg+volrcUxOA1kdUKIS4UQnwrBmQAIAgAChf4FEUACOOCFUpwHpfg4lOI8sDtakhCiD3PbCxH7wEzMfHwCju05c6t7a0wMhk14Uk7NyBIL5+xm9+qjUVMnQEq1I3Vs+yLBLKYCMApJVJGqrOY8RORk5lIiigPwHtTVBgCcC7UpSpn56QEwoKI3ZuatRJRc/hwREYAtAFoDWBEaNztkBMSH5v8JqqfACeAFAD8Q0Q3MvC90fQGAuQB6QPUArADQ5KSsgiwAB8rmtdhNkzoPyvpk99pcZ0XlPxsEScAFI5pyk46JdGTXDuWzR58Wc/ftibiAO29pCb5/7UVcPu5heJbOhnJkj9YiGZwC9pZC9pZCPhrtJQZ0gsWOmHs+UZZ8/imO7dlVoa1Ib0kJpj9yr9i8ey9cPPpudnRN54JZ24VgXu3P5HH2rOslordhKP9qoSoegMEAEpn5k9CxxMxBIkoE8D0zdy83dhMzn3OW9z8PQD+oHoAWABoCmAE1I+DJkOyukAEwFKpylwD0hFrieC2AWQAKmHkREZkB9IFqpFwK9QP0E9QUpoXM7A3N+ziAr5k5OySK6PcGD855fV36kd3VmNIkAD2HNUHL7slckHOIF3zynnB4W4WdJJpx4U1j5FYd2iN/bKuIM1IMDKKN+Kd/lXP9Znzx1MOV+r4JgoRL75mgNO7QRShZeEAp+f2gALl25gwKdgnpD3bxCmaxbNG2V2uZykNEaQAKmdkXOi5LK49YquIBmAvgcyKax8zHQso/A8D7AP5XNoiI6gE4XO71A8x8TwXuH/JZIgXAdQC+AvAngEkARgOIAfB0aOzPoS0CC4AroKb6TWXm8uH7IwD8B+rKPwPqFkDb0DytAEwmomYA6pVT/gAgS2bx2U4DGj73/VvZ1dLqtfOghmjbJ51dhXk8Z/Jzwr7stbXGRbdw2odiVvu3OfaW17j4vbtrjdwGBrUNx6V3guq2EOfec2ul76EoQXz3v2eE9CbNMfT+xzi9Uzrnz9xK/v21r+Ois2e9IBT+koiuA7ADwN4aSD9fByAPasbYUJzQMQBQzMyXhcatB5AL1dt8N9RaBOMAvEZEjQAsAjCYiHKZeUYl33KNU+nodlZdB/cB6EdEGUS0GMCbAF5i5i8AIJQC+AOA6aHLjgCoqO/YEvpZAKAv1PSnIQBugpoeOK3cWArJ5Au9NpdX/kTUBary90I1APyhn7LXA4noAqjpgeNOFkQQ6KP6zRM4LqVqJQHa9KmPG1/sqjTv6uD570ymKfffLuzLXlule4YbORDANy89RebzRpC1+1CtxTEwiErEtEawDX8EcyY/D09J1T2PR3Zuw9u3jRTX/fkDJd/cGgnDmspkqT1OPDKLcJ5XN/j0i89+CCCDmb8K/coL4H5m7gfgJQBTQq/vx4msr7PhYOj65QDMAGYDWAxgDNSt7DIOhMYdgrqYvBlqj48yvVjCzK8BGEREEVtRttpKARORpcz1Ue6cCEBkZn+5Y1OZu702EfDLL25ffuSu36dvs5zttc27pqHbkEyZKCgs/PQj2rp4IZhrd65u0649ecBtd1PBxPMhH654t0EDA4MzIEhI/L8tSvbiJfhj+kfVnoIck5yCKx98So5LShMLv9oBz8bIT+119qqnxPbN/EG0mVagZtPPV4fu1Q5qIHlrqEXjPgUwiZmvCY1bBmArgIbM3DvklegGtax3HDN/Fxr3V1p5Vd5/TVFtKV0nK//QORknAgP/cVybMJnF/zXvmj52xdw9cBdVLP6kYdtk9BreUDZbSVz02VRh44KfSZG1KypUnexYvoTSGzeV2z3xE+WPbSXASBMzMKgW4p/8ST6el0+LZ35SI/VHSvJy8fG4MWK7iwai98j/sqNrHaVg9nZRruBzLeyIhNg+GT7BKj0G4O6TMsokAB/iNOnnlZjtGDMPIKK5ZxiXx8yjiGg2ET0G1SvdPDSvl4i2MPMOAMsAPIq/e6wjBqPTW8U5wswfdh6YdcZWrnWbxuPaSZ3kfqOaYN2Ps+ndMTcg+5d5UaP8y1j82VTx6IEDnPD0glpp1BkYRBoxo19jObmh8PXzTwiKXLNfq/U//4C3x1xLub6DSLv/XDh71GVEYFSPo3M6g2gl1MDuk3kLwHaoe/FdodaOcYbOvVGJ6cwnHV8M4DyoWwCNyp2PJ6JJUDPQngltB3wLdYu6H4CySlkeqDFpEYnRDfDsSAv65T3TH19mKy34px2QkunEhdc3k2OTreLq779WVs35SvB7an/qzb9hsTtww8tvsbjldy56/SbDoDQwqCS2vqNgv/55TH/oPuQfPnjG8dVJVvtOGHTHeIXcQMHMrULgSIR49ERCnYe6ukWHqQ+AFaEUwAfKpZ/Pxd/Tz9ejXPo5M1c4/RwAiOgQ1ADDNgCGAbgIJwoOdWXmD0PjvoAaVzYdwD6ogeQtAewH4AbwGzO/EKqPM5SZn63Eu69xjAf22XGUgXfOHdTwbzEMcak2XDmhgzz0/nbYl70QH4y9CX/Omh71yh8AfG4XZj/zCEmdBgn2AbdpLY6BQa1EatIZjhtewHf/ey7syh8A9qxbjTdvHSFs37KUUm5vj7iBWTKZtFcPjnPTmARajVDtF6hB5cMAgJmLAPRh5t5Qs782MHNPZu4dOpd5NnMRkQPAptC1Zf2rc5h5IzNvBPAtEVlD2WbFoVg2Zub7mPkSAHMAjGbmi5n5hdD1w6EaKRGJ9v/DtQyTWXy2eZd0JSbRCnucGZff01a5+uGOOLZnJT68ezR+/+R9sTqidmsT+YcO4oc3XoJ9xBMwteh+5gsMDAz+QohPR9zD3/Ciz6YqmmYFKQrmv/MqTX3oLqC5FekPnMuWJvHaySMSYi9q4BFs0vhyZ+dCjaxPBYBy6ecz8C/p56FgwTPRB8BaIsqC2jFWwYnqtoCajn4b1GyxbCJKgpqO/te05W92mrTyiMLYAqgEAb/8orfEd5/VKYq716yQF82YIhYdM7qhdRlyldz18mFCwQPdScndp7U4BgaRj2hC4luble3r1vNP774eUXl53YaNQLfLr4ZvW4Fc+G34+wo4uqZz3CVZSwSb1Kv8eSKqDzXobhFU9/xxAK8z86+h3/cG8BrUku5TQtlnY0NpeaeFiIYB2AW12FwdqDVnPoLacwYAXFBjAf4DYB6A1wGsZuZxoes/APAGM68PHY8BMIOZI3ZFaBgAlSMxGPAfmfvqC6Zdq5ZrLUtE0WfULfI5PXpSwV1tBfbUvmIjBmdmwmov1uXLiDMTjnkZfplR3yGgyM9onyjihU5WrUWsNSS8uFTO9wv0+aQHazzorzLYY+Mx7MEn5eR6mWLhd7vYveZYWMIEySygzoNd3ILd1BvAytOOi/L085rG2AKoHPlgPNm6z0UREikTOSz45H1xz8aNiH95uQLp5IBag2jAIgKPt7Ng1gV23NbcjOENTZh1gR2Pt7NAjMAo8kgl7pHvFLcpTvjquUkRqfwBwF1ciGkP3SXOe38yYgY1QMpt7RQxqeYNvJgLMoIQaD7+RfkDp08/L1P+5Y4N5X8KDA9A5bEHvN6Dnz85MeHorh1ayxJRkCBg2MQnlPSUeM6/t6MIJTIfbgaV47F1XizPPbUHoFOSiGc7Gh6AMxF3/3QlkNWFpj90L9WWmCFBkjD43olKo7bnCiULDiglCw8KUKpffwixZqSPO9cjmMWWUCPsDWoIwwNQedyi2TTxwlG3urQWJNJgRcE3Lz4lFLiDlPDinzLIWBZGE0EFGJFlwoTWFgysJ6FXmoQJrS0YkWWCsZw4M3F3f8xK0x7CZ489UGuUPwAowSC+fekp4fOnH4S5SwKn39+JzRnVX+U2bkCWB8DbMJR/jWN4AKqG5Pe49343+fl6+9av0VqWiMNss+O6ZycrNvcxpeDh3hKMz1pUsKNYRo5H/b/8v21+DG9oQrJFNfLq2AhNYyMqli2iiB37HlP7ATT9oXtRnHtMa3GqRO8bRqND30Fwr8uVi37YI7Kv6p4+Ux0HUm5rVyyYxUwARVWX0uDfMAyAqjMs//ChT6bcd5ujttf3rwnscfG47rlXFfPxPUr+I32rrfS0gTYUBxjXL/b8tde/Pl9Gq3gBJkE9YRWB6b3sGkoYucSOeUsRuwwVZjxyHwpyDmstTrUQl5qGYROelGMTUsWCL3fAu7lqfQVSxrR1mTNiHySR3qwmEQ3+BcMAqDrkc7tWLv5saod1P31vbKmcAmdiEkY89ZJizj/ABY/0MZaHtZygwpAEQoGfcf0iN+b0PdElu/d8F36/uFq6ZkcVsaNfU6SeVwszHx+PvP17tRan2ulwyWBccM2N7N9fqhR8uUNUis++r4C1WQISR7Y4KFikRjhRzc+gBjEUVtVhi90xqte1o3z2OA2LZkQwpfnH8enEewVPTDolPLvQiAis5UgC4ZBbwR3LPLil2YlMjxyPgjSrEe9xMrF3vKNIPa8WZj/9SFQqfwBY++Mc/N/t11G+nEPp93eCo3uds+srIADxQ5q4BIt0OwzlHzYMD0A14fd6Xt29ZuUt37/2YsQ2ftAaq8OJ4ZOeZ2ewmAsn9hKMmIDaxyG3gtuXeZFgIdzcxIReaequzp/HgnhsnQ+jm5lxdUOTxlJGDnH3TlWo9YXCrCcmIu+APmLasjp2xqW3P6CgVEH+zG1C8OiZs6UdXdI5bmDWKsEqdQWMWNJwYRgA1YfT7/Xu/fr5SUkHt2zUWpaIxWyz4apHn+F4KcgFD54nIMo6JOoBn8ywnJTwLzNDZsAsGB6AMuImfqkoDZoECSoAAB7ASURBVDrQZ49NoOJcnVUKFQQMuO1ebtHtfCr987BS/Mt+AcFTx0iRTUKd8Z09gk06D2pbX4MwYRgA1csVRceOTP3onjGOaGv9W51IZguumPiEkpocj/wHugnw1J5UKAODM0KE+EnzFW98Bs18fAK5iwq1lkgzUjIb4orxT8hWs1MomLWdfLv++bdIuLKp19Ymebpgkf6rgYi6Rpw0aZLWMkQTW4noYkVR6h/ettmIrzgNiixj65LfqV7bTkrqyIfYv3KOwKUFWotlYFB1LHYkvrJSLlSswuePTyBvqb7LYbuLCrH6h28E0SpRk+G9Ya7jkH27iwQOqN4Ac2YM4gZklQoWaRAAo1pfmDGUVPXCFrvjpu5XXOOPSUrRWpaIRg4G8fULT4hbly9H/HOL2dz+Iq1FMjCoEkJyBpLe2CgfyS3C549PIJ/bqBFWxp9fzMB7Y0fBFeNC+vjOsHdIZQiExOHNXYJZvA2Aft0kGmJ4AKqfAkWRLcn1MztvXbLQiIb6N5ixe81Kwe/1cLNbnyIK+hHYbjRXMqh9mNv1RdzjP/Cu9euUOZOfl+SgsQV4MgGfF+t/nScU5x9Dq6v7w9mtLgS7aQ1Jwn1ay6ZXjBiAmsHq93h2zX31hbp71q3SWpZaQWabdrj8/ocRWDVHKX5jtOGZMqg1OIbeD9sVE7B45jRlzQ/fGp/dCpBYLwM3vPiGLEhSZwBrtZZHrxgf1prBa7bZbuw/5i63ZDI64lWE/RvW49OJ90Jp1ZcSnvtDhmAUDTSIfOIe+EyxXD4O37z4FAzlX3H633qXS2F+GIby1xTjA1tz/GSyWBZ0v3KEUdSighTkHMLUB8ZSvmylxLc2y0J8utYiGRicGlsMEl5dI/szOtC0CXdh/8b1WktUa2jdpz8nZzTYL5lMr2gti94xDIAaxGJ3/LfDgMGeus1aaC1KrcHrKsXnkyYI27OzkfDqWrZ0G6K1SAYGf0PKaIWkNzYqhw4fxyfj76SiYzrL8a8CjoRE9Lnhv16L3X4NACNQQmMMA6BmOWKyWG8YfN9DbpPVKBBYURRZxk/vvC7+/MFbcN72LuLu/1QxWgobRAL2S+9E/DMLsHLe9/jmpafEoM+ntUi1iotvvctNgvA6gGytZTEwggDDgt/jnrZz1fJh8958xbACzpK4tHQMeeBRJcZh5aKnB4vywa1ai2SgR2yxSHhsroy0xuLc11+C0f777GnZqw/3u/n2/WabrTkAw3KKAAwPQBgw2+y3NencraBplx5ai1LrKDp6BNMm3C1sXLoUCc8uhGPouP9v777DoyoTNYC/3ylzZs5MGmlAaCGgIE2QpggWREUFBVSwLerqYlsLFiyLvXPtrsty1bUCrsBdG7qoFFFAVJpghNBrwARImTPllO/+QbjLdXVFBE4y8/6eZ57M5JmQN3lIzptvvuJ3JEozRu9zkPtCqdxW4+DlG0fx4n8Asgub4JQrrokHQqGzwYt/vcERgMOnd8KyZv5t9FWh6K6dfmdpkFp26YazbrgNKF/j7L7/DE3G0nuXNTrEVA1Zt0z0tA4nKLNemSCXz/qYr0MdAEXVcMljz0RzmhTdpWraM37noX/hCMDhs0BR1SfPuuE2i69nH5gNSxfhbzddhQpbE43+XOoZPQf5HYlSlN62JxqNL3Ot/HZ4fcwN4MX/wPW76NJkRm7+AlXTnvU7C/1/HAE4vLREzFo876032i/68F3V7zANlhA4+rSzvH4XjFTs0s+9qnHDFThcbUkHR+YfnpWBviPENx+8486fMlH1XNfvSA1Wyy7dcPboOyv1YLAdgAq/89D/xwJw+LWxE/Glb9452qzcvNHvLA1aRl4+zrjuZq+gWXMRfekGEZ83ze9I1IDpR/RExk1vejHHE9Ofe0JsK+OE09/CzMrGZU+OjwUjkTMAzPY7D/07FgAfeK57RdWO8qdfveXaMPcM/+2OOqG/PPnSUUKWl7lVj52veru2+R2JGpJQBrJGvyH1I48V86ZMlIumvyP4V/9vJATOv/vhaOOSts/rRvB2v+PQT+McAB8oqvqSmZXzRd8LL0v6nSUVfDfnU/HS9Vdg3dZdaPTMEmSMfNTvSNRARM6/C7njy+R2L0P+bfRV+Pq9abz4HwQ9zhriFBSXrNaN4J/8zkI/jyMA/smz4/HS6c8/kbf6q/l+Z0kZRe07YOA1o2UwoErr1duU+BdT/I5E9VCg04mIXPvfri10Zcb4ZwUP7Tp4CkvaYvg9j9TqRrAjgA1+56GfxwLgr+7JeOyziXfdHOJ8gINHUVV0POlU2e/CkUJWbXdrnrtCddZ843csqgfU/BbIHP2GozZrpy2a/q67YOpk1bE5EHewBEIhXPbkeCvSKHckALbveo4FwGee646M7t71wqu3XmsmolG/46QU3Qiix9nDvO5nDVGctYu9qqcvVSTnB6SnQAhZf3zR048eoKxfusiZ88bLWtX2cr9TpZxBo++Iterc7e1AKDTS7yz0y1gA6oFkPPZC+ZqykVMe+JMpped3nJRjZmXj+BG/89r16afYy2bK6r9cI2QtN2NKC4qKjIsfRODkS+UPG9d7M1+ZoO5Yt8bvVCmp19Dhdo/Bw8qMkNkdQMzvPPTLWADqBy1hWXOXffpRt8/eeDngd5hUldOkCCddeqXbrF0H1fnuM6/qr9dzRCBVaQFkjnwE+vEXyN0/7JCzX3tR2bSC588cKm16Hoszrr25Ug8GOwPY6nce2j8sAPVHXjIeXzFj/DP5K+fP5a5jh1CTtkfiuPMvdoqObK+565a41ROu5yFDKUJEGiHr9/8l1a4DUVNZIWe9/pLCvfsPrYJWrTH8vsesQDDUDwAn2zQgLAD1y9F2PP7FpLtvNX/YsM7vLCmvUVEz9B46wm3b81jV2bbarX1ptGp/zxUZDZHa9AhkXvmMq5Yco/6wcb2zYOpkjTP7Dz0zKxsjxz1vhTIyRwpF4aS/BoYFoJ6Rnjfcqq56+dVbrjVjNdV+x0kLkZxcdB801O3c/zTV3VXuWhP/pCa+fNfvWLQfAl36I3zJI67WuFhd/dUCZ+E7UzSW58ND0wO48KEnotmNmz6hG8Y9fuehX48FoB6y4/Enf9iwbtTke8eY0uOkwMPFMMPocuoZXo/BwxQkY5678B9K9eQHgeguv6PRvoSAefooGGfd6Ipwlrr4o/e9xR+9p1hVu/1OllYG3XRHrFWXrjMCIXMIAF5IGiAWgPpJTVjWrNK5M3t++vJ4w+8w6UZRNZR074VuAwe5jUuOUJ3ytU58+vNabOZrfkdLa3qnExEZOsZTio8WsdpafPk/b6F07mzBdfyHX++hI+weg4euCoTMHuCM/waLBaD+yknGY0vmT5nU9Ov3pml+h0lXGbl56HDiKd7Rp54pNF2DXLVA1ky+X3HWLfU7WlpQGxcjMnysVDqeLJVAUCn9fI777awZ6vY1ZX5HS1ttex0nB14zeqceDHYCwGU0DRgLQP3W3I7HF3368l9yV8z5lCsD/CQEmh/VCUefdqbTumt3za2tcp1F09XaqePgVW7yO11KEWYmwkNuhd7nfEfLzNU2fbfMWfrxh9q6xd/Ac3l4lp8Kiksw4t7HLD0Y7AuAyysaOBaA+q+dnYh/+cEzj2eu+Wah31kIgBEOo0333mh//IluUbsOql1V4cnv5ijR6S+AIwMHRuQ0QeSMq6H1PNvVcovU3du3ukv+OV1ZOe8zEY/W+h2PAGTmF+LiR56yguEIZ/ynCBaAhqGHHY/PmvboveHNpcv9zkL70AwDrTp1Rbs+/dzibj1Uz0562FIq43MmqbHPJgF23O+I9ZOiInjcMIROuECi5dFSMzOUratKvZXz5yrrFn+NmsoKvxPSPsI5jXDxI09boUjGHaquP+t3Hjo4WAAajv7JWOy9t+4dE9qxfq3fWegnCEVBUbsOOKJ3H/fI3scLPRhS3Jqdjlz7jRqb+5ZILHwXSOOft0CHvgj1uwDqUX1tkVWgO4mEXP3VAq9s4Tx103ffwrVtvyPSTwhlZOKih5+ywtnZD2sB4yG/89DBwwLQgEjPG5awrNffvGt0aHc5d9us7zLy8tGsfUe06tzVbdGxsxKMZAp7Z7mH9YuVxJKPEf/yXchoii5d0w0Y3U5HqOcgoE1PV8spVD3PQ/maMmf9km/UtYu/Fju3cO5EfWeYYVz44BPRjLz8P+uGMcbvPHRwsQA0MK5jj4rV1Dz5xh03mtFdPNCmITGzstGsfQe06HS016rT0Yjk5it2zJKwdjvYvkazVy0UiSWfwC5b2KBGCpTcIhhd+iPQ7lioLTs7MqdI0SOZSk3FDrnpu+Vy04plytay78HT9xoW3QhixP2PR7MbN3k9EAxdA671TzksAA2QnUiMrd1Vefubd97EI4QbMN0IIrdZC+S1aImC4hK3cUlbNGraTFU1HY5V46Fqh5TbVqpO+Vq429bA2fw97I0rgIR12LMqec2hNW0LvbgL9JKuQGEbD1kF0MNZCgRQtWO7V7Fpg7dj/Vp1W9lKUb6mDHacy8MbKk0P4LyxD1l5LVpNC4RCvwMv/imJBaBhEsl47LldW7dcOvmeMWEnmfA7Dx1Eocws5DVvibwWrZDXrIWXVVAoM3LzhJmdowSCIbi2DTcZ96Sd8JREVAprp+JVVahe1Q/waiog4xaknYB0EpB2EnCSgOcBqgahqICqAoq657EehBLJgRLJhpJVIEVWgSdDWZ40worQDaHoAUULBOC5HhLRWln1w3Zvx/p1qNi4Tt25dTN2btmM6G7ulJhKFFXD0NvvsZq0PfLjQMgcBsD1OxMdGiwADZeSiFmTKjasO2vKw3ebToIlIB0IRUE4OwcZuXmI5OQikpsHMzNLBsMRLxiJwDDDMEwTqh6AputQ9QBUXROQgOs68BwHruNI17GFaztwHRuuY0vXtqVVXY2aih/UaNUuYVXthlW1G9Hde96yZKYHIRQMvvnOWPOOnT83QuaZADgzM4WxADRsajJmvb5r29bBf7//znAydviHhokoRQiBgdeOjpd077XICJn9AXANa4pT/A5Av4kbCJkX5zQtmnjBA+OioYxMv/MQUUMkBAZceV2iTfdepUbIPBW8+KcFjgCkBmHH409Eq3aNmjT2VpOnohHR/lJUDWdef2usZZeuK4yQeQqAKr8z0eHBApA6hJ1I3B2vrb5t0tjbzJrKH/zOQ0T1nGYYGDLmHquwuM08wzQHgyf7pRUWgBTj2MmbE9Ho/ZPvvs3cvZ0HdRHRTzPCYZw39qFoTuOm7wdC5sUAeNJSmmEBSEGuY1+VjMWenHzPmBB3WyOiHwvnNMLwex+1wtk5LweCoRsAeH5nosOPBSBFea57cTIem/D2A3eFdqxb43ccIqonsgubYPh9j1mGGX5UN4wHwU1+0hYLQAqTnjckmYi/Me3hu82tq773Ow4R+Sy/ZTHOv/vhmB4MjlY1fbzfechfLACp73Q7Hp/60finQ6vmfy78DkNE/ihq1wFDbr/HChjBkUJRpvidh/zHApAejrbj8Y+/en9a1vy3J+p+hyGiw6t1tx4464YxUT0YPAfAJ37nofqBBSB9NE7ErI83LFtc8uFzT4QcO+l3HiI6DLoNHOwef8HvoroRHABgod95qP5gAUgvoUTMmlj9w44BUx8aG+YhLkSpS9V1nDrq+nib7r23BEKhUwGs9TsT1S8sAOlH2InEPXYifuvUh8aaO9bzdwJRqonk5GLoHfdGM/MLZxmmOQIAzw2nf8MCkKak553rJJOvTv/zE+bqhfP9jkNEB0mTtu0w5PZ7YpoeeFQ3jAfAZX70M1gA0tsxdjz+zy/feTvry2lvaX6HIaLfpuNJp3onX/YHSzeCIwB84Hceqt9YAKhpImZ9sn7JN8Uf/vnJoGvz+G+ihkZRVfS//OpEuz4n7AiEQgMArPQ7E9V/LAAEAGbCsibX7qw4+R/jHgzvLt/qdx4i2k+hzCwMGXN3tFHT5l8apjkUPM2P9hMLAO0lXMe5znXsxz558YVg6dxZ3DSIqJ4rKC7BsDvus3Qj+LweDN4B7ulPvwILAP1Yl2Qs9u7aRQvzZ0x4PmTHeTooUX3U9fRBXt8LR8Y0PXApd/ajA8ECQD8lnIhZf01a1pB/jHvA5GFCRPVHODsHZ15/m1VQXLLOMM0hAMr8zkQNEwsA/SzpecMdO/ni55NfDy2a/o7qdx6idFfSvRcGXjs6pqjqM7oRvBsAZ+3SAWMBoF/SOhGz3i1fvar4g2ceN2M11X7nIUo7uhFE/8uvirft3acqEAwNBTDP70zU8LEA0P7Q7Xj8McdOjnrvqUfNTSuW+Z2HKG00LjkCg2++0zJM891AyBwFgC2cDgoWAPo1BtiJ+N8Xf/S+Oe/vbwRcx/E7D1HKEoqC3kOH2z0GD4vrRvByAJzoRwcVCwD9WoUJy3o9Xlt93AfPjgtvK+N+I0QHW1ZBIQaNvjOaXdh4mWGGzwOwxe9MlHpYAOhACADn2Yn4hG9nzgjOnfSq4SQSfmciavCEUND5lNO8fhdfnlBU9W5NDzwJru2nQ4QFgH6L3IRl/dVOxAd++PwT5sblS/3OQ9RgFRSX4PRrbqrNzCtYbZjmJQCW+52JUhsLAB0MZ9jx+Gurv15gznplQogrBYj2XyBkot9FlyaO6ndyQtX0mxRVfQX8q58OAxYAOlgykvHYI9KTl89+7cXg8tkfC/D/FtF/1O64fuj/+6stRVWnBkLmTQAq/c5E6YMFgA62rgnLen3Xti2tPvrL0+HKTRv8zkNU7+Q0KcJpV98QzW/RalsgZF4CYIHfmSj9sADQoaB6rjvKdZzHl33yoT5/yqRAwor6nYnId5oeQO9zR9jdBp5tK4ryJ1XXnwPA9bTkCxYAOpQaJ2LWOAEMmzdlkrHko/cU7h1A6aq4a3ecNup6SzOMTw0zfDW4tI98xgJAh8NR8Wj0OddO9p792ovm9/M+A+cHULrIb1mMk0ZeGS1s3bYqEApdBmCG35mIABYAOrxOSFjRv9Tu2tl85svjI1w2SKksM78Q/S66zGrdrbutavqfFFWdACDpdy6ivVgA6HATAM5NxmLPbl9bljHzlQnhio3r/c5EdNCEMjJx7LkXJjqeNMAVAk9qAeNxADV+5yL6MRYA8kvAc92rXMd5cPVX87W5E18J1VRW+J2J6IAZ4TB6DBpmdxs42JWQrweCobEAtvudi+jnsACQ3zLtROIuAH9c+vF09at3pwasqt1+ZyLab4FQCMecOcTpPmiIIz05zTDNuwCs9zsX0S9hAaD6oigZi90nFOXC77+YIxa+MyW4u3yr35mIfpZuBNF14CC31znnJaWUHxhm+E4AZX7nItpfLABU3+Q7yeRNUso/bly+RMyfOjm8fQ1/p1L9EcnJRbczBttdBpzhSul9bJjhMQBK/c5F9GuxAFB9FfFc9wrHTt5VsXFDcN6UiZENSxf5nYnSWEFxCXqefZ5VckwP4bne64FQaByA1X7nIjpQLABU3+kARiQs636ralfevLcnRlbOnwvp8awUOvSEUND6mB7oPXR4bW5Ri6SiqY+rmj4BwC6/sxH9ViwA1FAIAAPj0dr7Pcdpv2DaW8FvZ81QnETC71yUgnQjiA4nniJ7nXOepRvBjUY4fD+AqQBsv7MRHSwsANQQ9YpHa+9TNe2ElfM/x9KPpwfLV6/yOxOlgIzcPHQbODjZecBAz3PdOcFw5AEA8wDwFyWlHBYAasiauo5zmWsnr43VVGcs/uh987vPZiqxmmq/c1EDomoairv1QOf+p9c2P6qT6nnuq4FgaByAtX5nIzqUWAAoFQgA/RJW9DpV08/auHyJu/ifH4Q3LF0MKTlXgH5ak7ZHouNJAxLt+pwgPcdZEYxkvABgCgA2SEoLLACUarKk9C5IWNaN0vOaL/3kw8DymTO0qh3ckI2AzPwCHNXvZLfzKafHAkGzWtP1v6q6/hq4cQ+lIRYASmWdk/H4VUIRl1RsWC+/nfnPjNVff4lYdZXfuegwCoRCOKLX8ehy6sCavOYtFc91JwdC5n8DWAi+tk9pjAWA0oEBYHC8tnakZgT6V27elCz9fHbG6q/mi6rt5X5no0MgEAqhZaeuaHf8CVbrrj1Ux07ODYYjLwCYDoBLR4jAAkDpxwBwciJmjVAU9Ryrapco/XxOqGzhPG3HujV+Z6PfIKdJUxR37SGPPLZvTWFxSTAZj30TjGS8IYT4OwCeNEX0IywAlM4UAL3sROI8z3VHeI6dtXLB5+qqBV8YW75fAc91/c5H/4Giamh2VAeUHNM7eUSv45KGGXY8z33fMMNTAHwKoNbvjET1GQsA0R4CQDvPdYYmY7GLFU1vtXH5EmfD0sWRTaXLUbl5I8CfFd+ZWdlo3bUH2vY+rrZ5h866m0yu1YOht1RNexfAEvA1faL9xgJA9NOaAeifsKKnAeIkoYicrStLE+uXLsrYXLpc7Fi/ltsRHwbZhU3Q9Mj2aNa+Y7x5h07JSKNc3UkkZwcjkbcAfAhgh98ZiRoqFgCi/dMUQN9kLDbA89xTND3QpHxNWXz90m8im79brpSvWQXXcfzO2KBpAQOFJW1QdER72aJjl5ombY8MQAjLc5wFwUjGDADzASwGt+MlOihYAIh+RAjRSkq5/heelgugr52I93dt5zTdMFpVbt5obVu9Krhj/RqjYtMGVGzcgGTMOiQZK2st5EbMQ/JvHxZCIDMvH03atkOzdkclmnfsEs8ubGLa8dhqVdNn6cHgbOy54G/2OSlRymIBINqHEGIMgDIp5TQhxHgAPbDn5Lcm2LOCYD2AHABfSSmv2udDMwH0BNAxEY32ktLrqgdDxQkraldu2uiWr1lllq9dre3auhk7t2z6t9EC1/OgKsr/PX5vaSlO73gEvt1cDiuZxHFtWmHq199i2DGdoCgCc1etQ9gIoFvLokP57fjNVF1HTuOmaFTUDI2KmnsFLVtbuc1buln5BabnulHHTn4djGTMEELMA7AIQMzvzETpggWAqI4QohWAW6SU19U9fhrAP6SUs4UQlwJoJqV8UAhxIoBz9z7vP1AAFAPoJD2v05AhQ64oLy8vNAwjUFlZISsrKkWT/Fzh2DYahU38/tQTUbl9O75dsxbLN5cjLxJGzLaRsB1EggFsr67FMS2L0KJRNiJBA28uWIxhx3RCUNcO2fdkfyiqCjMzG1kFhWhU1Ay5zVrY+a1aW7lNm6mhzMygHU+Ue567MhAMfaPq+goA39fduOUukY9YAIjqCCHGApgipSyte/w0gL746RGA+ftRAH6OcdFFF128adOmc+bMmbMwGbPaeq7bSghRtH7TpsKrr7nWvPL3lyfiNTVerKZaxKqr9HhtjRarqcachV/juHZt0CY/F4tXr0FVdQ2OOQSjAKqmwczK/r9bOCsHZnY2Ijm5yYy8vEQkJ9czs7KVYCRD1wK67ibtWse2twlFLDPM8CIhxPcASgGsA1+zJ6qXWACI6gghXpFSXrrP4+cBfIc9Q9NnAsgH8AqAbgA6SimvOYDP8ayU8vq6UYTTpZS3171flVK6QojmhmH8Qdf1EwA003Vd1zRNC+h6IBAIBG684fqqTp066X369NF3V1WHHnn00eB/jRvnOMmk7SQTrus4nmvb0rGTcJNJOMkkHMcWqqZJVdOh6jpUTYOq6VA1TSiaJlRNUxRV3XNTVFWoqgIATjJZ7Tn2TunJcqGILZoR3KDp+lYA2/e5lQPYCYBLIogaGBYAojo/UQDaA9j75/Vt2HPx37vsbMvekYJf+TmWSSk71xWA17DnyFkB4BMp5QP7PO9DAC/96MOvk1KeuM9zggCeklLegj1zEMLYM0oR3Oft3vvJfW6J/bhvg2vqiVKavy8eEtUvMSFEREpZK4TIAjABwN7tALtjzwV273B2DMDAA/gc+/6lPHHvCMBPGAsgD8Dx2HNxn173vn0VA9gEIFp3IyLabywARP8yHcAwAK9KKauEECdJKR0hRCMAH0gp++x9ohBixaEKIYR4AHtectgJoACADuAoAJoQYpeUckjdU88H8D+HKgcRpTbll59ClDbeB3CmEKIAAOou/s0BTATw5N4nCSGKAGzde79usuB/JITQhBA/+/MmhAgIIUTdQxfAbVLKUwDcD+CZuvvnoW4EQghxBIAiKeWyA/g6iYg4AkC0l5RSCiFGAzhFCDEXwCQAlQDGSSk/BYC61+6fAfBU3YeVY89M918yBMD1AHYLIWbvfec+9w0AF2HPnIAy/GuugQZAF0L0BXAf/lVETgZwy6/+IomI6nASINHPEEIYUsrEj96nAlCllMl9HutSyrgfGYmIDhQLABERURriHAAiIqI0xAJARESUhlgAiIiI0hALABERURpiASAiIkpDLABERERpiAWAiIgoDbEAEBERpSEWACIiojTEAkBERJSGWACIiIjSEAsAERFRGmIBICIiSkMsAERERGmIBYCIiCgNsQAQERGlIRYAIiKiNMQCQERElIZYAIiIiNIQCwAREVEaYgEgIiJKQ/8LzoQyl60d3NYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.rcParams['font.family'] = ['SimHei'] \n",
    "colors = plt.get_cmap(\"tab20c\")(np.arange(2)*4)\n",
    "data0 = data.groupby('性别')['count'].sum()\n",
    "data1 = data.groupby(['性别', 'BMI group'])['count'].sum()\n",
    "data0.plot.pie(radius=0.7, colors=colors, labeldistance=0.7, wedgeprops=dict(width=0.3, edgecolor='w'))    \n",
    "data1.plot.pie(radius=1, labeldistance=1, wedgeprops=dict(width=0.3, edgecolor='w'))\n",
    "plt.axis('square')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
